Exploratory Data Analysis - AltMetric research paper

In this file we are going to perform an exploratory data analysis for AlMetric data. Please have in mind the methodology in use, i.e. CRISP-DM have some stages such as data retention, data cleaning and etc. which is necessary for EDA, except data retention which is done via AlMetric platform, other necessary steps will be done in this notebook.

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from sklearn import preprocessing
pd.options.display.max_columns = 200
sns.set_theme(style='darkgrid')

Data Gathering

In this section we are going to import the gathered data in the code for further analysis.

atu = pd.read_csv('data/allameh.csv')
atu['University'] = "Allameh Tabataba'i University"

aut = pd.read_csv('data/amirkabir.csv')
aut['University'] = "Amir Kabir University"

sbu = pd.read_csv('data/beheshti.csv')
sbu['University'] = "Shahid Beheshti University"

fum = pd.read_csv('data/ferdowsi.csv')
fum['University'] = "Ferdowsi University of Mashhad"

ugui = pd.read_csv('data/guilan.csv')
ugui['University'] = "University of Guilan"

ihu = pd.read_csv('data/imamhosein.csv')
ihu['University'] = "Imam Hossein University"

uisf = pd.read_csv('data/isfahan.csv')
uisf['University'] = "University of Isfahan"

iut = pd.read_csv('data/iut.csv')
iut['University'] = "Isfahan University of Technology"

knu = pd.read_csv('data/knu.csv')
knu['University'] = "K. N. Toosi University of Technology"

sut = pd.read_csv('data/sharif.csv')
sut['University'] = "Sharif University of Technology"

ushi = pd.read_csv('data/shiraz.csv')
ushi['University'] = "University of Shiraz"

iust = pd.read_csv('data/stu.csv')
iust['University'] = "Iran University of Science and Technology"

utab = pd.read_csv('data/tabriz.csv')
utab['University'] = "University of Tabriz"

tmu = pd.read_csv('data/tarbiatmodares.csv')
tmu['University'] = "Tarbiat Modares University"

uteh = pd.read_csv('data/ut.csv')
uteh['University'] = "University of Tehran"

Data Transformation & Feature Engineering

In the next section we are going to transform imported data to our needs and some feature generations are in order, i.e. label encoding two categorical variables, renaming features for standard pandas practice and etc. Another important thing we are doing in the cell below, is that we will split the column "Subjects_FoR" to main categories, to the length of the record with the highest category items.

On the context of categories, it's worth to mention some explanation regarding the matter. Based on the retrieved data, the Feature "Subjects_FoR" contains all categories for each article, these categories are based on standard system of categorization of AltMetric, the categories which starts with a 2 digit number, are major categories and those one which starts with 4 digit numbers are the sub categories of their corresponding major cateogry. For this scientific endeavor we will only focus on the major categories.

As we explored this feature, we found out that the article with the most major categories actually have 7 major categories, thus we create 7 columns one for each major categories, obviously the categories with greater number are more probable to have "No Category" as value.

df = pd.concat([atu, aut, sbu, fum, ugui, ihu, uisf, iut,
                knu, sut, ushi, iust, utab, tmu, uteh])

df.drop(['Authors at my Institution', 'Departments', 'Journal ISSNs', 'Sustainable Development Goals', 'ISBN', 'National Clinical Trial ID', 'URI', 'PubMed ID', 'PubMedCentral ID', 'Handle.net IDs', 'ADS Bibcode', 'arXiv ID', 'RePEc ID', 'SSRN', 'URN', 'Details Page URL', 'Badge URL', 'Syllabi mentions', 'DOI', 'Funder'], axis=1, inplace=True)

df.columns = df.columns.str.replace(' ', '_')
df = df.reset_index(drop=True)

df['Subjects_(FoR)'].fillna('00 No Category', inplace=True)
df['Journal/Collection_Title'].fillna('No Title', inplace=True)
main_lst = []
for record in df['Subjects_(FoR)'].str.split('; '):
    temp_lst = []
    for element in record:
        if len(element.split(' ')[0]) == 2:
            temp_lst.append(element)
    main_lst.append(temp_lst)
temp_df = pd.DataFrame(main_lst, columns=[
    'Category_1',
    'Category_2',
    'Category_3',
    'Category_4',
    'Category_5',
    'Category_6',
    'Category_7',
])
temp_df.fillna('00 No Category', inplace=True)
df = pd.concat([df, temp_df], axis=1)

label_encoder = preprocessing.LabelEncoder()
df['Journal/Collection_Title_LE'] = label_encoder.fit_transform(df['Journal/Collection_Title'])
df['Output_Type_LE'] = label_encoder.fit_transform(df['Output_Type'])
df['OA_Status_LE'] = label_encoder.fit_transform(df['OA_Status'])
df['OA_Type_LE'] = label_encoder.fit_transform(df['OA_Type'])
df['Publisher_Names_LE'] = label_encoder.fit_transform(df['Publisher_Names'])
df['University_LE'] = label_encoder.fit_transform(df['University'])
df['Category_1_LE'] = label_encoder.fit_transform(df['Category_1'])
df['Category_2_LE'] = label_encoder.fit_transform(df['Category_2'])
df['Category_3_LE'] = label_encoder.fit_transform(df['Category_3'])
df['Category_4_LE'] = label_encoder.fit_transform(df['Category_4'])
df['Category_5_LE'] = label_encoder.fit_transform(df['Category_5'])
df['Category_6_LE'] = label_encoder.fit_transform(df['Category_6'])
df['Category_7_LE'] = label_encoder.fit_transform(df['Category_7'])
df['Publication_Date'] = pd.to_datetime(df['Publication_Date'])

df['OA_Type'].value_counts()

closed    48155
gold      11523
green      9690
bronze     2656
hybrid     1985
Name: OA_Type, dtype: int64

In the table below you can see the top 5 and bottom 5 records in the main dataframe. In this dataframe we have done the feature engineering generation phase.

This dataframe consists of some features that we will going to descrive each one of them:

• Altmetric_Attention_Score: This is the main feature of the dataset, this metric is the attention score for each article, this feature is seems like the aggregation of multiple features which we will discuss further in this section.
• Title: The title of article.
• Journal/Collection_Title: The journal or collection the article have published in. Types other than "Article" will not have title since they are not published in the journal or collection.
• Output_Type: This is a categorical feature, 4 classes are used in this feature: 1. Article, 2. Chapter, 3. Book, 4. News. The classes are self explanatory.
• OA_Status: Whether that article is published under open access license or not.
• OA_Type: A categorical feature determining the type of open access each class is discussed:
  1. Closed: This article is not published in open access journal.
  2. Gold: The most open and least restrictive type of open access.
  3. Green: This is when the accepted article is first deposited into a subject-based repository or an institution’s repository, which then often specifies how the article may be used.
  4. Bronze: This is not fully open access because although the article will be freely available, the types of open access journals that offer this kind of service have no open license.
  5. Hybrid: This is one of those types of open access where a subscription journal offers open access, where the processing fee is paid for individual articles. Although the processing fees may be higher than that of a regular open access journal, it may be worth the effort if your article fits the journal’s aims and scope perfectly.
• Subjects_(FoR): The categories used for each record, these categories are recorded as string and concatenated together.
• Affiliations_(GRID): The affiliations of the authors.
• Publication_Date: The date that record have been published.
• News_mentions: The number of mentions that the corresponding record had in the News.
• Blog_mentions: The number of mentions that the corresponding record had in the Blog.
• Policy_mentions: The number of mentions that record used in the Policies.
• Patent_mentions: The number of times that the corresponding record was mentioned in the Patents.
• Twitter_mentions: The number of mentions that the corresponding record had in the Twitter.
• Peer_review_mentions: The number of mentions that the corresponding record had in the Peer Review.
• Weibo_mentions: The number of mentions that the corresponding record had in the Weibo.
• Facebook_mentions: The number of mentions that the corresponding record had in the Facebook.
• Wikipedia_mentions: The number of mentions that the corresponding record had in the Wikipedia.
• Google+_mentions: The number of mentions that the corresponding record had in the Google+.
• Linkedin_mentions: The number of mentions that the corresponding record had in the LinkedIn.
• Reddit_mentions: The number of mentions that the corresponding record had in the Reddit.
• Pinterest_mentions: The number of mentions that the corresponding record had in the Pinterest.
• F1000_mentions: The number of mentions that the corresponding record had in the F1000. F1000 is a open research publisher for scientists, scholars, and clinical researchers.
• Q&A_mentions: The number of mentions that the corresponding record had in the Q&A Services.
• Video_mentions: The number of mentions that the corresponding record had in the Video.
• Number_of_Mendeley_readers: The number of Mendeley users that have added a particular document to a Mendeley library.
• Number_of_Dimensions_citations: Dimensions extracts references between publications either from existing databases (such as CrossRef, PubMed Central or OpenCitations data), or directly from the full text record provided by the content publisher. Reference extraction is not limited to journal items, but also includes citations from and to books, conference proceedings and pre-prints.
• Publisher_Names: The name of publisher which have published the record.
• University: The name of university that this record related to.
• Category_1: The First major category used in this record.
• Category_2: The Second major category used in this record.
• Category_3: The Third major category used in this record.
• Category_4: The Fourth major category used in this record.
• Category_5: The Fifth major category used in this record.
• Category_6: The Sixth major category used in this record.
• Category_7: The Seventh major category used in this record.

Features with LE at their end is label encoded version of a corresponding feature.

df

	Altmetric_Attention_Score	Title	Journal/Collection_Title	Output_Type	OA_Status	OA_Type	Subjects_(FoR)	Affiliations_(GRID)	Publication_Date	News_mentions	Blog_mentions	Policy_mentions	Patent_mentions	Twitter_mentions	Peer_review_mentions	Weibo_mentions	Facebook_mentions	Wikipedia_mentions	Google+_mentions	LinkedIn_mentions	Reddit_mentions	Pinterest_mentions	F1000_mentions	Q&A_mentions	Video_mentions	Number_of_Mendeley_readers	Number_of_Dimensions_citations	Publisher_Names	University	Category_1	Category_2	Category_3	Category_4	Category_5	Category_6	Category_7	Journal/Collection_Title_LE	Output_Type_LE	OA_Status_LE	OA_Type_LE	Publisher_Names_LE	University_LE	Category_1_LE	Category_2_LE	Category_3_LE	Category_4_LE	Category_5_LE	Category_6_LE	Category_7_LE
0	1629	COVID-19 and male reproductive function: a pro...	Reproduction	Article	True	bronze	11 Medical and Health Sciences; 1114 Paediatri...	Allameh Tabataba'i University; University of G...	2021-03-01	87	1	0	0	2537	0	0	2	1	0	0	0	0	0	0	0	112	50	NaN	Allameh Tabataba'i University	11 Medical and Health Sciences	31 Biological Sciences	32 Biomedical and Clinical Sciences	00 No Category	00 No Category	00 No Category	00 No Category	7146	0	1	0	189	0	11	23	22	0	0	0	0
1	906	The effects of three different exercise modali...	Reproduction	Article	True	bronze	11 Medical and Health Sciences; 1103 Clinical ...	Academic Center for Education, Culture and Res...	2017-02-01	128	1	0	0	22	0	0	6	0	2	0	1	0	0	0	0	117	38	NaN	Allameh Tabataba'i University	11 Medical and Health Sciences	31 Biological Sciences	32 Biomedical and Clinical Sciences	00 No Category	00 No Category	00 No Category	00 No Category	7146	0	1	0	189	0	11	23	22	0	0	0	0
2	239	Fear, Loss, Social Isolation, and Incomplete G...	Basic And Clinical Neuroscience	Article	True	gold	11 Medical and Health Sciences; 1117 Public He...	Allameh Tabataba'i University; Charles R. Drew...	2020-07-30	30	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	190	52	NaN	Allameh Tabataba'i University	11 Medical and Health Sciences	42 Health Sciences	00 No Category	00 No Category	00 No Category	00 No Category	00 No Category	1048	0	1	2	189	0	11	34	0	0	0	0	0
3	175	Foundations of Social Policy and Welfare in Islam	No Title	Chapter	False	closed	16 Studies in Human Society; 1605 Policy and A...	Allameh Tabataba'i University	2020-12-19	21	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	7	2	Springer Nature	Allameh Tabataba'i University	16 Studies in Human Society	22 Philosophy and Religious Studies	50 Philosophy and Religious Studies	00 No Category	00 No Category	00 No Category	00 No Category	6346	2	0	1	171	0	16	21	39	0	0	0	0
4	102	Laughter yoga versus group exercise program in...	International Journal of Geriatric Psychiatry	Article	True	green	11 Medical and Health Sciences; 1103 Clinical ...	Allameh Tabataba'i University; Imam Khomeini H...	2010-09-16	9	3	2	0	3	0	0	0	1	0	0	0	0	0	0	0	317	131	ESSOAr; Natural History Museum; Wiley	Allameh Tabataba'i University	11 Medical and Health Sciences	32 Biomedical and Clinical Sciences	42 Health Sciences	00 No Category	00 No Category	00 No Category	00 No Category	3846	0	1	3	75	0	11	24	32	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
74004	0	AN AHP MODEL FOR CROP PLANNING WITHIN IRRIGATI...	Irrigation & Drainage	Article	False	closed	09 Engineering; 0905 Civil Engineering; 30 Agr...	University of Tehran	2011-09-29	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	18	7	ESSOAr; Natural History Museum; Wiley	University of Tehran	09 Engineering	30 Agricultural, Veterinary and Food Sciences	40 Engineering	00 No Category	00 No Category	00 No Category	00 No Category	4211	0	0	1	75	14	9	22	30	0	0	0	0
74005	0	GC–MS Determination of PAHs in Fish Samples Fo...	Chromatographia	Article	False	closed	03 Chemical Sciences; 0301 Analytical Chemistr...	University of Tehran	2011-07-14	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	12	14	Springer Nature	University of Tehran	03 Chemical Sciences	34 Chemical Sciences	00 No Category	00 No Category	00 No Category	00 No Category	00 No Category	1620	0	0	1	171	14	3	26	0	0	0	0	0
74006	0	Interface thermal resistance and thermal recti...	Applied Physics Letters	Article	False	closed	02 Physical Sciences; 09 Engineering; 10 Techn...	University of Tehran	2011-08-01	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	81	103	American Institute of Physics (AIP)	University of Tehran	02 Physical Sciences	09 Engineering	10 Technology	51 Physical Sciences	00 No Category	00 No Category	00 No Category	732	0	0	1	12	14	2	8	7	24	0	0	0
74007	0	The systematic importance of anatomical data i...	Botanical Journal of the Linnean Society	Article	True	bronze	06 Biological Sciences; 0603 Evolutionary Biol...	Queen Mary University of London; Royal Botanic...	2010-10-11	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	23	7	Oxford University Press (OUP); Wiley	University of Tehran	06 Biological Sciences	31 Biological Sciences	00 No Category	00 No Category	00 No Category	00 No Category	00 No Category	1267	0	1	0	150	14	6	23	0	0	0	0	0
74008	0	Pollen morphology of the genus Gagea (Liliacea...	Flora	Article	False	closed	06 Biological Sciences; 0607 Plant Biology; 31...	University of Tehran	2005-04-01	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	24	20	Elsevier BV	University of Tehran	06 Biological Sciences	31 Biological Sciences	00 No Category	00 No Category	00 No Category	00 No Category	00 No Category	2723	0	0	1	78	14	6	23	0	0	0	0	0

74009 rows × 49 columns

Data Exploration

First thing first, we must compare the number of article for each university. In the plot below, you can see the number of articles per university, sorted in a descending order.

As you can see the top university on the number of research output is University of Tehran with more than 16,000 articles. In the second and third place are Tarbiat Modares University and Sharif University of Technology. The lowest three universities on the context of research output are: Imam Hossein University, Allameh Tabataba'i University and University of Guilan.

x = list(df['University'].value_counts().index)
y = list(df['University'].value_counts())
plt.figure(figsize=(7, 4))
ax = sns.barplot(data=df, x=x, y=y)
ax.bar_label(ax.containers[0])
plt.title('Number of Articles for each University')
plt.xticks(rotation=90)
plt.show()

In the plot below, you can see the top 30 journal or collection in which universities have published their research output.

The first place is No Title, this fact is due to the some of research outputs are not articles published in journals, they are books, chapters and news, this kind of articles have no corresponding journal title and this feature is due to this fact.

The journal with the most hosted articles are Scientific Reports which is a open access scientific journal under Nature and was the 5th most cited journal in the world.

On the second and third place we have Journal of High Energy Physics and PLOS ONE.

x = list(df['Journal/Collection_Title'].value_counts().head(30).index)
y = list(df['Journal/Collection_Title'].value_counts().head(30))
plt.figure(figsize=(15, 5))
ax = sns.barplot(x=x, y=y)
ax.bar_label(ax.containers[0])
plt.title('Number of Articles for top 30 Collection / Journal')
plt.xticks(rotation=90)
plt.show()

In the plot below, you can see the top 30 Publisher in which universities have published their research output.

The publisher with the most articles of these universities are Elsevier BV with more than 21,000 and in the second place with more than 13,000 articles is Springer Nature.

x = list(df['Publisher_Names'].value_counts().head(30).index)
y = list(df['Publisher_Names'].value_counts().head(30))
plt.figure(figsize=(15, 5))
ax = sns.barplot(x=x, y=y)
ax.bar_label(ax.containers[0])
plt.title('Number of Articles for top 30 Publisher')
plt.xticks(rotation=90)
plt.show()

Categories Exploration

In the following section we will explore the categories quantity status. The plot in the following cell, is the barplot of the 7 category columns.

• More than 16,000 articles published had Enigneering as first categories, Medical and Health Sciences and Chemical Sciences is in the second and third place.
• It's Interesting to mention that the Engineering category used as first category is coded 09 but the Engneering category used in other than first places are coded 40.
• More than 17,000 articles had Engineering as second category, Biomedical and Clinical Sciences was the second most used category as second category.
• Majority of articles (65.4%) had just two categories.
• The similar trend of having the Engineering as the most used category is tracked among the other places of category, it is used more than 5,900 times as third category.
• Around 9.6% of articles had more than three major categories.
• Engineering is the most used major category as 4th category also.
• 1.8% of articles have 5 and more major categories, 0.3% of articles have 6 and more major categories and just 3 articles have 7 major categories, which all of them had Mathematical Sciences as their 7th major category.

fig, axes = plt.subplots(7, 1, figsize=(20, 50))
x = [
    list(df['Category_1'].value_counts().index),
    list(df['Category_2'].value_counts().index),
    list(df['Category_3'].value_counts().index),
    list(df['Category_4'].value_counts().index),
    list(df['Category_5'].value_counts().index),
    list(df['Category_6'].value_counts().index),
    list(df['Category_7'].value_counts().index),

]
y = [
    list(df['Category_1'].value_counts()),
    list(df['Category_2'].value_counts()),
    list(df['Category_3'].value_counts()),
    list(df['Category_4'].value_counts()),
    list(df['Category_5'].value_counts()),
    list(df['Category_6'].value_counts()),
    list(df['Category_7'].value_counts()),
]

sns.barplot(ax=axes[0], x=x[0], y=y[0])
axes[0].set_title('Number of First Categories used in Articles')
axes[0].bar_label(axes[0].containers[0])
axes[0].tick_params(labelrotation=90)

sns.barplot(ax=axes[1], x=x[1], y=y[1])
axes[1].set_title('Number of Second Categories used in Articles')
axes[1].bar_label(axes[1].containers[0])
axes[1].tick_params(labelrotation=90)

sns.barplot(ax=axes[2], x=x[2], y=y[2])
axes[2].set_title('Number of Third Categories used in Articles')
axes[2].bar_label(axes[2].containers[0])
axes[2].tick_params(labelrotation=90)

sns.barplot(ax=axes[3], x=x[3], y=y[3])
axes[3].set_title('Number of Fourth Categories used in Articles')
axes[3].bar_label(axes[3].containers[0])
axes[3].tick_params(labelrotation=90)

sns.barplot(ax=axes[4], x=x[4], y=y[4])
axes[4].set_title('Number of Fifth Categories used in Articles')
axes[4].bar_label(axes[4].containers[0])
axes[4].tick_params(labelrotation=90)

sns.barplot(ax=axes[5], x=x[5], y=y[5])
axes[5].set_title('Number of Sixth Categories used in Articles')
axes[5].bar_label(axes[5].containers[0])
axes[5].tick_params(labelrotation=90)

sns.barplot(ax=axes[6], x=x[6], y=y[6])
axes[6].set_title('Number of Seventh Categories used in Articles')
axes[6].bar_label(axes[6].containers[0])
axes[6].tick_params(labelrotation=90)

fig.tight_layout()
plt.show()

In the Cells Below we will explore the 7 Categories for each University.

Allameh Tabataba'i University top 7 Categories

• 25.1% of published articles had Medical and Health Sciences as first major category.
• Engineering is the 8th most used first category in the articles of the ATU.
• This might be and indicator toward the Engineering articles, meaning the attention score by altmetric might be biased towards these type of articles, which can be rooted to the interest of media and news agency towards engineering breakthroughs.
• Majority of articles published by ATU (63.1%) had two and less major categories.
• No articles published by ATU had 7 major categories.

fig, axes = plt.subplots(3, 3, figsize=(20, 20))
x = [
    list(df[df['University'] == "Allameh Tabataba'i University"]['Category_1'].value_counts().index),
    list(df[df['University'] == "Allameh Tabataba'i University"]['Category_2'].value_counts().index),
    list(df[df['University'] == "Allameh Tabataba'i University"]['Category_3'].value_counts().index),
    list(df[df['University'] == "Allameh Tabataba'i University"]['Category_4'].value_counts().index),
    list(df[df['University'] == "Allameh Tabataba'i University"]['Category_5'].value_counts().index),
    list(df[df['University'] == "Allameh Tabataba'i University"]['Category_6'].value_counts().index),
    list(df[df['University'] == "Allameh Tabataba'i University"]['Category_7'].value_counts().index),

]
y = [
    list(df[df['University'] == "Allameh Tabataba'i University"]['Category_1'].value_counts()),
    list(df[df['University'] == "Allameh Tabataba'i University"]['Category_2'].value_counts()),
    list(df[df['University'] == "Allameh Tabataba'i University"]['Category_3'].value_counts()),
    list(df[df['University'] == "Allameh Tabataba'i University"]['Category_4'].value_counts()),
    list(df[df['University'] == "Allameh Tabataba'i University"]['Category_5'].value_counts()),
    list(df[df['University'] == "Allameh Tabataba'i University"]['Category_6'].value_counts()),
    list(df[df['University'] == "Allameh Tabataba'i University"]['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0, 0], x=x[0], y=y[0])
axes[0, 0].set_title("Number of First Categories used in Articles of Allameh Tabataba'i University")
axes[0, 0].bar_label(axes[0, 0].containers[0])
axes[0, 0].tick_params(labelrotation=90)

sns.barplot(ax=axes[0, 1], x=x[1], y=y[1])
axes[0, 1].set_title("Number of Second Categories used in Articles of Allameh Tabataba'i University")
axes[0, 1].bar_label(axes[0, 1].containers[0])
axes[0, 1].tick_params(labelrotation=90)

sns.barplot(ax=axes[0, 2], x=x[2], y=y[2])
axes[0, 2].set_title("Number of Third Categories used in Articles of Allameh Tabataba'i University")
axes[0, 2].bar_label(axes[0, 2].containers[0])
axes[0, 2].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 0], x=x[3], y=y[3])
axes[1, 0].set_title("Number of Fourth Categories used in Articles of Allameh Tabataba'i University")
axes[1, 0].bar_label(axes[1, 0].containers[0])
axes[1, 0].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 1], x=x[4], y=y[4])
axes[1, 1].set_title("Number of Fifth Categories used in Articles of Allameh Tabataba'i University")
axes[1, 1].bar_label(axes[1, 1].containers[0])
axes[1, 1].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 2], x=x[5], y=y[5])
axes[1, 2].set_title("Number of Sixth Categories used in Articles of Allameh Tabataba'i University")
axes[1, 2].bar_label(axes[1, 2].containers[0])
axes[1, 2].tick_params(labelrotation=90)

sns.barplot(ax=axes[2, 1], x=x[6], y=y[6])
axes[2, 1].set_title("Number of Seventh Categories used in Articles of Allameh Tabataba'i University")
axes[2, 1].bar_label(axes[2, 1].containers[0])
axes[2, 1].tick_params(labelrotation=90)

fig.delaxes(axes[2, 0])
fig.delaxes(axes[2, 2])

fig.tight_layout()
plt.show()

Amir Kabir University Top 7 Categories

• 38.3% of published articles of AUT have Engineering as their first category, Information and Computing Sciences and Chemical Sciences are in the second and third place.
• 42.4% of published articles of AUT have Engineering as their second category and like the first item, Information and Computing Sciences and Chemical Sciences are in second and third place.
• The majority of published articles of AUT (65.6%) have one or two major categories.

fig, axes = plt.subplots(3, 3, figsize=(20, 20))
x = [
    list(df[df['University'] == "Amir Kabir University"]['Category_1'].value_counts().index),
    list(df[df['University'] == "Amir Kabir University"]['Category_2'].value_counts().index),
    list(df[df['University'] == "Amir Kabir University"]['Category_3'].value_counts().index),
    list(df[df['University'] == "Amir Kabir University"]['Category_4'].value_counts().index),
    list(df[df['University'] == "Amir Kabir University"]['Category_5'].value_counts().index),
    list(df[df['University'] == "Amir Kabir University"]['Category_6'].value_counts().index),
    list(df[df['University'] == "Amir Kabir University"]['Category_7'].value_counts().index),

]
y = [
    list(df[df['University'] == "Amir Kabir University"]['Category_1'].value_counts()),
    list(df[df['University'] == "Amir Kabir University"]['Category_2'].value_counts()),
    list(df[df['University'] == "Amir Kabir University"]['Category_3'].value_counts()),
    list(df[df['University'] == "Amir Kabir University"]['Category_4'].value_counts()),
    list(df[df['University'] == "Amir Kabir University"]['Category_5'].value_counts()),
    list(df[df['University'] == "Amir Kabir University"]['Category_6'].value_counts()),
    list(df[df['University'] == "Amir Kabir University"]['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0, 0], x=x[0], y=y[0])
axes[0, 0].set_title("Number of First Categories used in Articles of Amir Kabir University")
axes[0, 0].bar_label(axes[0, 0].containers[0], rotation=90)
axes[0, 0].tick_params(labelrotation=90)

sns.barplot(ax=axes[0, 1], x=x[1], y=y[1])
axes[0, 1].set_title("Number of Second Categories used in Articles of Amir Kabir University")
axes[0, 1].bar_label(axes[0, 1].containers[0], rotation=90)
axes[0, 1].tick_params(labelrotation=90)

sns.barplot(ax=axes[0, 2], x=x[2], y=y[2])
axes[0, 2].set_title("Number of Third Categories used in Articles of Amir Kabir University")
axes[0, 2].bar_label(axes[0, 2].containers[0], rotation=90)
axes[0, 2].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 0], x=x[3], y=y[3])
axes[1, 0].set_title("Number of Fourth Categories used in Articles of Amir Kabir University")
axes[1, 0].bar_label(axes[1, 0].containers[0])
axes[1, 0].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 1], x=x[4], y=y[4])
axes[1, 1].set_title("Number of Fifth Categories used in Articles of Amir Kabir University")
axes[1, 1].bar_label(axes[1, 1].containers[0])
axes[1, 1].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 2], x=x[5], y=y[5])
axes[1, 2].set_title("Number of Sixth Categories used in Articles of Amir Kabir University")
axes[1, 2].bar_label(axes[1, 2].containers[0])
axes[1, 2].tick_params(labelrotation=90)

sns.barplot(ax=axes[2, 1], x=x[6], y=y[6])
axes[2, 1].set_title("Number of Seventh Categories used in Articles of Amir Kabir University")
axes[2, 1].bar_label(axes[2, 1].containers[0])
axes[2, 1].tick_params(labelrotation=90)

fig.delaxes(axes[2, 0])
fig.delaxes(axes[2, 2])

fig.tight_layout()
plt.show()

Shahid Beheshti University top 7 Categories

• The distribution of categories in SBU is much more smoother, in other words, SBU is academically more engaged in different subjects.
• The top category of published articles in SBU is Medical and Health Sciences. In the second and third place are Chemical Sciences and Engineering.
• The majority of published articles in SBU (65.5%) has less than two or one major categories.

fig, axes = plt.subplots(3, 3, figsize=(20, 20))
x = [
    list(df[df['University'] == "Shahid Beheshti University"]['Category_1'].value_counts().index),
    list(df[df['University'] == "Shahid Beheshti University"]['Category_2'].value_counts().index),
    list(df[df['University'] == "Shahid Beheshti University"]['Category_3'].value_counts().index),
    list(df[df['University'] == "Shahid Beheshti University"]['Category_4'].value_counts().index),
    list(df[df['University'] == "Shahid Beheshti University"]['Category_5'].value_counts().index),
    list(df[df['University'] == "Shahid Beheshti University"]['Category_6'].value_counts().index),
    list(df[df['University'] == "Shahid Beheshti University"]['Category_7'].value_counts().index),

]
y = [
    list(df[df['University'] == "Shahid Beheshti University"]['Category_1'].value_counts()),
    list(df[df['University'] == "Shahid Beheshti University"]['Category_2'].value_counts()),
    list(df[df['University'] == "Shahid Beheshti University"]['Category_3'].value_counts()),
    list(df[df['University'] == "Shahid Beheshti University"]['Category_4'].value_counts()),
    list(df[df['University'] == "Shahid Beheshti University"]['Category_5'].value_counts()),
    list(df[df['University'] == "Shahid Beheshti University"]['Category_6'].value_counts()),
    list(df[df['University'] == "Shahid Beheshti University"]['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0, 0], x=x[0], y=y[0])
axes[0, 0].set_title("Number of First Categories used in Articles of Shahid Beheshti University")
axes[0, 0].bar_label(axes[0, 0].containers[0], rotation=90)
axes[0, 0].tick_params(labelrotation=90)

sns.barplot(ax=axes[0, 1], x=x[1], y=y[1])
axes[0, 1].set_title("Number of Second Categories used in Articles of Shahid Beheshti University")
axes[0, 1].bar_label(axes[0, 1].containers[0], rotation=90)
axes[0, 1].tick_params(labelrotation=90)

sns.barplot(ax=axes[0, 2], x=x[2], y=y[2])
axes[0, 2].set_title("Number of Third Categories used in Articles of Shahid Beheshti University")
axes[0, 2].bar_label(axes[0, 2].containers[0], rotation=90)
axes[0, 2].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 0], x=x[3], y=y[3])
axes[1, 0].set_title("Number of Fourth Categories used in Articles of Shahid Beheshti University")
axes[1, 0].bar_label(axes[1, 0].containers[0])
axes[1, 0].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 1], x=x[4], y=y[4])
axes[1, 1].set_title("Number of Fifth Categories used in Articles of Shahid Beheshti University")
axes[1, 1].bar_label(axes[1, 1].containers[0])
axes[1, 1].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 2], x=x[5], y=y[5])
axes[1, 2].set_title("Number of Sixth Categories used in Articles of Shahid Beheshti University")
axes[1, 2].bar_label(axes[1, 2].containers[0])
axes[1, 2].tick_params(labelrotation=90)

sns.barplot(ax=axes[2, 1], x=x[6], y=y[6])
axes[2, 1].set_title("Number of Seventh Categories used in Articles of Shahid Beheshti University")
axes[2, 1].bar_label(axes[2, 1].containers[0])
axes[2, 1].tick_params(labelrotation=90)

fig.delaxes(axes[2, 0])
fig.delaxes(axes[2, 2])

fig.tight_layout()
plt.show()

Ferdowsi University of Mashhad top 7 Categories

• The most used first category in published artciles of this university is Engineering.
• Biological Sciences and Medical and Health Sciences are in the second and third place.
• Most of the published articles of this university (68.4%) has two or one major categories.

fig, axes = plt.subplots(3, 3, figsize=(20, 20))
x = [
    list(df[df['University'] == "Ferdowsi University of Mashhad"]['Category_1'].value_counts().index),
    list(df[df['University'] == "Ferdowsi University of Mashhad"]['Category_2'].value_counts().index),
    list(df[df['University'] == "Ferdowsi University of Mashhad"]['Category_3'].value_counts().index),
    list(df[df['University'] == "Ferdowsi University of Mashhad"]['Category_4'].value_counts().index),
    list(df[df['University'] == "Ferdowsi University of Mashhad"]['Category_5'].value_counts().index),
    list(df[df['University'] == "Ferdowsi University of Mashhad"]['Category_6'].value_counts().index),
    list(df[df['University'] == "Ferdowsi University of Mashhad"]['Category_7'].value_counts().index),

]
y = [
    list(df[df['University'] == "Ferdowsi University of Mashhad"]['Category_1'].value_counts()),
    list(df[df['University'] == "Ferdowsi University of Mashhad"]['Category_2'].value_counts()),
    list(df[df['University'] == "Ferdowsi University of Mashhad"]['Category_3'].value_counts()),
    list(df[df['University'] == "Ferdowsi University of Mashhad"]['Category_4'].value_counts()),
    list(df[df['University'] == "Ferdowsi University of Mashhad"]['Category_5'].value_counts()),
    list(df[df['University'] == "Ferdowsi University of Mashhad"]['Category_6'].value_counts()),
    list(df[df['University'] == "Ferdowsi University of Mashhad"]['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0, 0], x=x[0], y=y[0])
axes[0, 0].set_title("Number of First Categories used in Articles of Ferdowsi University of Mashhad")
axes[0, 0].bar_label(axes[0, 0].containers[0], rotation=90)
axes[0, 0].tick_params(labelrotation=90)

sns.barplot(ax=axes[0, 1], x=x[1], y=y[1])
axes[0, 1].set_title("Number of Second Categories used in Articles of Ferdowsi University of Mashhad")
axes[0, 1].bar_label(axes[0, 1].containers[0], rotation=90)
axes[0, 1].tick_params(labelrotation=90)

sns.barplot(ax=axes[0, 2], x=x[2], y=y[2])
axes[0, 2].set_title("Number of Third Categories used in Articles of Ferdowsi University of Mashhad")
axes[0, 2].bar_label(axes[0, 2].containers[0], rotation=90)
axes[0, 2].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 0], x=x[3], y=y[3])
axes[1, 0].set_title("Number of Fourth Categories used in Articles of Ferdowsi University of Mashhad")
axes[1, 0].bar_label(axes[1, 0].containers[0])
axes[1, 0].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 1], x=x[4], y=y[4])
axes[1, 1].set_title("Number of Fifth Categories used in Articles of Ferdowsi University of Mashhad")
axes[1, 1].bar_label(axes[1, 1].containers[0])
axes[1, 1].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 2], x=x[5], y=y[5])
axes[1, 2].set_title("Number of Sixth Categories used in Articles of Ferdowsi University of Mashhad")
axes[1, 2].bar_label(axes[1, 2].containers[0])
axes[1, 2].tick_params(labelrotation=90)

sns.barplot(ax=axes[2, 1], x=x[6], y=y[6])
axes[2, 1].set_title("Number of Seventh Categories used in Articles of Ferdowsi University of Mashhad")
axes[2, 1].bar_label(axes[2, 1].containers[0])
axes[2, 1].tick_params(labelrotation=90)

fig.delaxes(axes[2, 0])
fig.delaxes(axes[2, 2])

fig.tight_layout()
plt.show()

University of Guilan top 7 Categories

• This university also has more evenly distributed articles among categories.
• Still the most published articles from this university are categorized into Engineering.

fig, axes = plt.subplots(3, 3, figsize=(20, 20))
x = [
    list(df[df['University'] == "University of Guilan"]['Category_1'].value_counts().index),
    list(df[df['University'] == "University of Guilan"]['Category_2'].value_counts().index),
    list(df[df['University'] == "University of Guilan"]['Category_3'].value_counts().index),
    list(df[df['University'] == "University of Guilan"]['Category_4'].value_counts().index),
    list(df[df['University'] == "University of Guilan"]['Category_5'].value_counts().index),
    list(df[df['University'] == "University of Guilan"]['Category_6'].value_counts().index),
    list(df[df['University'] == "University of Guilan"]['Category_7'].value_counts().index),

]
y = [
    list(df[df['University'] == "University of Guilan"]['Category_1'].value_counts()),
    list(df[df['University'] == "University of Guilan"]['Category_2'].value_counts()),
    list(df[df['University'] == "University of Guilan"]['Category_3'].value_counts()),
    list(df[df['University'] == "University of Guilan"]['Category_4'].value_counts()),
    list(df[df['University'] == "University of Guilan"]['Category_5'].value_counts()),
    list(df[df['University'] == "University of Guilan"]['Category_6'].value_counts()),
    list(df[df['University'] == "University of Guilan"]['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0, 0], x=x[0], y=y[0])
axes[0, 0].set_title("Number of First Categories used in Articles of University of Guilan")
axes[0, 0].bar_label(axes[0, 0].containers[0], rotation=90)
axes[0, 0].tick_params(labelrotation=90)

sns.barplot(ax=axes[0, 1], x=x[1], y=y[1])
axes[0, 1].set_title("Number of Second Categories used in Articles of University of Guilan")
axes[0, 1].bar_label(axes[0, 1].containers[0], rotation=90)
axes[0, 1].tick_params(labelrotation=90)

sns.barplot(ax=axes[0, 2], x=x[2], y=y[2])
axes[0, 2].set_title("Number of Third Categories used in Articles of University of Guilan")
axes[0, 2].bar_label(axes[0, 2].containers[0], rotation=90)
axes[0, 2].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 0], x=x[3], y=y[3])
axes[1, 0].set_title("Number of Fourth Categories used in Articles of University of Guilan")
axes[1, 0].bar_label(axes[1, 0].containers[0])
axes[1, 0].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 1], x=x[4], y=y[4])
axes[1, 1].set_title("Number of Fifth Categories used in Articles of University of Guilan")
axes[1, 1].bar_label(axes[1, 1].containers[0])
axes[1, 1].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 2], x=x[5], y=y[5])
axes[1, 2].set_title("Number of Sixth Categories used in Articles of University of Guilan")
axes[1, 2].bar_label(axes[1, 2].containers[0])
axes[1, 2].tick_params(labelrotation=90)

sns.barplot(ax=axes[2, 1], x=x[6], y=y[6])
axes[2, 1].set_title("Number of Seventh Categories used in Articles of University of Guilan")
axes[2, 1].bar_label(axes[2, 1].containers[0])
axes[2, 1].tick_params(labelrotation=90)

fig.delaxes(axes[2, 0])
fig.delaxes(axes[2, 2])

fig.tight_layout()
plt.show()

Imam Hossein University top 7 Categories

• This university has the lowest amount of published articles in contrast of other universities.
• Unlike most of the other universities, the most published articles from this university are categorized into Chemical Sciences.

fig, axes = plt.subplots(3, 3, figsize=(20, 20))
x = [
    list(df[df['University'] == "Imam Hossein University"]['Category_1'].value_counts().index),
    list(df[df['University'] == "Imam Hossein University"]['Category_2'].value_counts().index),
    list(df[df['University'] == "Imam Hossein University"]['Category_3'].value_counts().index),
    list(df[df['University'] == "Imam Hossein University"]['Category_4'].value_counts().index),
    list(df[df['University'] == "Imam Hossein University"]['Category_5'].value_counts().index),
    list(df[df['University'] == "Imam Hossein University"]['Category_6'].value_counts().index),
    list(df[df['University'] == "Imam Hossein University"]['Category_7'].value_counts().index),

]
y = [
    list(df[df['University'] == "Imam Hossein University"]['Category_1'].value_counts()),
    list(df[df['University'] == "Imam Hossein University"]['Category_2'].value_counts()),
    list(df[df['University'] == "Imam Hossein University"]['Category_3'].value_counts()),
    list(df[df['University'] == "Imam Hossein University"]['Category_4'].value_counts()),
    list(df[df['University'] == "Imam Hossein University"]['Category_5'].value_counts()),
    list(df[df['University'] == "Imam Hossein University"]['Category_6'].value_counts()),
    list(df[df['University'] == "Imam Hossein University"]['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0, 0], x=x[0], y=y[0])
axes[0, 0].set_title("Number of First Categories used in Articles of Imam Hossein University")
axes[0, 0].bar_label(axes[0, 0].containers[0], rotation=90)
axes[0, 0].tick_params(labelrotation=90)

sns.barplot(ax=axes[0, 1], x=x[1], y=y[1])
axes[0, 1].set_title("Number of Second Categories used in Articles of Imam Hossein University")
axes[0, 1].bar_label(axes[0, 1].containers[0], rotation=90)
axes[0, 1].tick_params(labelrotation=90)

sns.barplot(ax=axes[0, 2], x=x[2], y=y[2])
axes[0, 2].set_title("Number of Third Categories used in Articles of Imam Hossein University")
axes[0, 2].bar_label(axes[0, 2].containers[0], rotation=90)
axes[0, 2].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 0], x=x[3], y=y[3])
axes[1, 0].set_title("Number of Fourth Categories used in Articles of Imam Hossein University")
axes[1, 0].bar_label(axes[1, 0].containers[0])
axes[1, 0].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 1], x=x[4], y=y[4])
axes[1, 1].set_title("Number of Fifth Categories used in Articles of Imam Hossein University")
axes[1, 1].bar_label(axes[1, 1].containers[0])
axes[1, 1].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 2], x=x[5], y=y[5])
axes[1, 2].set_title("Number of Sixth Categories used in Articles of Imam Hossein University")
axes[1, 2].bar_label(axes[1, 2].containers[0])
axes[1, 2].tick_params(labelrotation=90)

sns.barplot(ax=axes[2, 1], x=x[6], y=y[6])
axes[2, 1].set_title("Number of Seventh Categories used in Articles of Imam Hossein University")
axes[2, 1].bar_label(axes[2, 1].containers[0])
axes[2, 1].tick_params(labelrotation=90)

fig.delaxes(axes[2, 0])
fig.delaxes(axes[2, 2])

fig.tight_layout()
plt.show()

University of Isfahan top 7 Categories

• The most of articles published by this university is categorized into Medical and Health Sciences as their first major category.

fig, axes = plt.subplots(3, 3, figsize=(20, 20))
x = [
    list(df[df['University'] == "University of Isfahan"]['Category_1'].value_counts().index),
    list(df[df['University'] == "University of Isfahan"]['Category_2'].value_counts().index),
    list(df[df['University'] == "University of Isfahan"]['Category_3'].value_counts().index),
    list(df[df['University'] == "University of Isfahan"]['Category_4'].value_counts().index),
    list(df[df['University'] == "University of Isfahan"]['Category_5'].value_counts().index),
    list(df[df['University'] == "University of Isfahan"]['Category_6'].value_counts().index),
    list(df[df['University'] == "University of Isfahan"]['Category_7'].value_counts().index),

]
y = [
    list(df[df['University'] == "University of Isfahan"]['Category_1'].value_counts()),
    list(df[df['University'] == "University of Isfahan"]['Category_2'].value_counts()),
    list(df[df['University'] == "University of Isfahan"]['Category_3'].value_counts()),
    list(df[df['University'] == "University of Isfahan"]['Category_4'].value_counts()),
    list(df[df['University'] == "University of Isfahan"]['Category_5'].value_counts()),
    list(df[df['University'] == "University of Isfahan"]['Category_6'].value_counts()),
    list(df[df['University'] == "University of Isfahan"]['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0, 0], x=x[0], y=y[0])
axes[0, 0].set_title("Number of First Categories used in Articles of University of Isfahan")
axes[0, 0].bar_label(axes[0, 0].containers[0], rotation=90)
axes[0, 0].tick_params(labelrotation=90)

sns.barplot(ax=axes[0, 1], x=x[1], y=y[1])
axes[0, 1].set_title("Number of Second Categories used in Articles of University of Isfahan")
axes[0, 1].bar_label(axes[0, 1].containers[0], rotation=90)
axes[0, 1].tick_params(labelrotation=90)

sns.barplot(ax=axes[0, 2], x=x[2], y=y[2])
axes[0, 2].set_title("Number of Third Categories used in Articles of University of Isfahan")
axes[0, 2].bar_label(axes[0, 2].containers[0], rotation=90)
axes[0, 2].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 0], x=x[3], y=y[3])
axes[1, 0].set_title("Number of Fourth Categories used in Articles of University of Isfahan")
axes[1, 0].bar_label(axes[1, 0].containers[0])
axes[1, 0].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 1], x=x[4], y=y[4])
axes[1, 1].set_title("Number of Fifth Categories used in Articles of University of Isfahan")
axes[1, 1].bar_label(axes[1, 1].containers[0])
axes[1, 1].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 2], x=x[5], y=y[5])
axes[1, 2].set_title("Number of Sixth Categories used in Articles of University of Isfahan")
axes[1, 2].bar_label(axes[1, 2].containers[0])
axes[1, 2].tick_params(labelrotation=90)

sns.barplot(ax=axes[2, 1], x=x[6], y=y[6])
axes[2, 1].set_title("Number of Seventh Categories used in Articles of University of Isfahan")
axes[2, 1].bar_label(axes[2, 1].containers[0])
axes[2, 1].tick_params(labelrotation=90)

fig.delaxes(axes[2, 0])
fig.delaxes(axes[2, 2])

fig.tight_layout()
plt.show()

Isfahan University of Technology top 7 Categories

• As it was predictable, since this university is more focused on the technical sciences, the most published articles of this university, categorized into Engineering as their first major category.
• the Engineering category as first or second major one, has a significant distance from other categories.

fig, axes = plt.subplots(3, 3, figsize=(20, 20))
x = [
    list(df[df['University'] == "Isfahan University of Technology"]['Category_1'].value_counts().index),
    list(df[df['University'] == "Isfahan University of Technology"]['Category_2'].value_counts().index),
    list(df[df['University'] == "Isfahan University of Technology"]['Category_3'].value_counts().index),
    list(df[df['University'] == "Isfahan University of Technology"]['Category_4'].value_counts().index),
    list(df[df['University'] == "Isfahan University of Technology"]['Category_5'].value_counts().index),
    list(df[df['University'] == "Isfahan University of Technology"]['Category_6'].value_counts().index),
    list(df[df['University'] == "Isfahan University of Technology"]['Category_7'].value_counts().index),

]
y = [
    list(df[df['University'] == "Isfahan University of Technology"]['Category_1'].value_counts()),
    list(df[df['University'] == "Isfahan University of Technology"]['Category_2'].value_counts()),
    list(df[df['University'] == "Isfahan University of Technology"]['Category_3'].value_counts()),
    list(df[df['University'] == "Isfahan University of Technology"]['Category_4'].value_counts()),
    list(df[df['University'] == "Isfahan University of Technology"]['Category_5'].value_counts()),
    list(df[df['University'] == "Isfahan University of Technology"]['Category_6'].value_counts()),
    list(df[df['University'] == "Isfahan University of Technology"]['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0, 0], x=x[0], y=y[0])
axes[0, 0].set_title("Number of First Categories used in Articles of Isfahan University of Technology")
axes[0, 0].bar_label(axes[0, 0].containers[0], rotation=90)
axes[0, 0].tick_params(labelrotation=90)

sns.barplot(ax=axes[0, 1], x=x[1], y=y[1])
axes[0, 1].set_title("Number of Second Categories used in Articles of Isfahan University of Technology")
axes[0, 1].bar_label(axes[0, 1].containers[0], rotation=90)
axes[0, 1].tick_params(labelrotation=90)

sns.barplot(ax=axes[0, 2], x=x[2], y=y[2])
axes[0, 2].set_title("Number of Third Categories used in Articles of Isfahan University of Technology")
axes[0, 2].bar_label(axes[0, 2].containers[0], rotation=90)
axes[0, 2].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 0], x=x[3], y=y[3])
axes[1, 0].set_title("Number of Fourth Categories used in Articles of Isfahan University of Technology")
axes[1, 0].bar_label(axes[1, 0].containers[0])
axes[1, 0].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 1], x=x[4], y=y[4])
axes[1, 1].set_title("Number of Fifth Categories used in Articles of Isfahan University of Technology")
axes[1, 1].bar_label(axes[1, 1].containers[0])
axes[1, 1].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 2], x=x[5], y=y[5])
axes[1, 2].set_title("Number of Sixth Categories used in Articles of Isfahan University of Technology")
axes[1, 2].bar_label(axes[1, 2].containers[0])
axes[1, 2].tick_params(labelrotation=90)

sns.barplot(ax=axes[2, 1], x=x[6], y=y[6])
axes[2, 1].set_title("Number of Seventh Categories used in Articles of Isfahan University of Technology")
axes[2, 1].bar_label(axes[2, 1].containers[0])
axes[2, 1].tick_params(labelrotation=90)

fig.delaxes(axes[2, 0])
fig.delaxes(axes[2, 2])

fig.tight_layout()
plt.show()

K. N. Toosi University of Technology top 7 Categories

• As it was predictable, since this university is more focused on the technical sciences, the most published articles of this university, categorized into Engineering as their first major category.
• the Engineering category as first or second major one, has a significant distance from other categories.

fig, axes = plt.subplots(3, 3, figsize=(20, 20))
x = [
    list(df[df['University'] == "K. N. Toosi University of Technology"]['Category_1'].value_counts().index),
    list(df[df['University'] == "K. N. Toosi University of Technology"]['Category_2'].value_counts().index),
    list(df[df['University'] == "K. N. Toosi University of Technology"]['Category_3'].value_counts().index),
    list(df[df['University'] == "K. N. Toosi University of Technology"]['Category_4'].value_counts().index),
    list(df[df['University'] == "K. N. Toosi University of Technology"]['Category_5'].value_counts().index),
    list(df[df['University'] == "K. N. Toosi University of Technology"]['Category_6'].value_counts().index),
    list(df[df['University'] == "K. N. Toosi University of Technology"]['Category_7'].value_counts().index),

]
y = [
    list(df[df['University'] == "K. N. Toosi University of Technology"]['Category_1'].value_counts()),
    list(df[df['University'] == "K. N. Toosi University of Technology"]['Category_2'].value_counts()),
    list(df[df['University'] == "K. N. Toosi University of Technology"]['Category_3'].value_counts()),
    list(df[df['University'] == "K. N. Toosi University of Technology"]['Category_4'].value_counts()),
    list(df[df['University'] == "K. N. Toosi University of Technology"]['Category_5'].value_counts()),
    list(df[df['University'] == "K. N. Toosi University of Technology"]['Category_6'].value_counts()),
    list(df[df['University'] == "K. N. Toosi University of Technology"]['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0, 0], x=x[0], y=y[0])
axes[0, 0].set_title("Number of First Categories used in Articles of K. N. Toosi University of Technology")
axes[0, 0].bar_label(axes[0, 0].containers[0], rotation=90)
axes[0, 0].tick_params(labelrotation=90)

sns.barplot(ax=axes[0, 1], x=x[1], y=y[1])
axes[0, 1].set_title("Number of Second Categories used in Articles of K. N. Toosi University of Technology")
axes[0, 1].bar_label(axes[0, 1].containers[0], rotation=90)
axes[0, 1].tick_params(labelrotation=90)

sns.barplot(ax=axes[0, 2], x=x[2], y=y[2])
axes[0, 2].set_title("Number of Third Categories used in Articles of K. N. Toosi University of Technology")
axes[0, 2].bar_label(axes[0, 2].containers[0], rotation=90)
axes[0, 2].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 0], x=x[3], y=y[3])
axes[1, 0].set_title("Number of Fourth Categories used in Articles of K. N. Toosi University of Technology")
axes[1, 0].bar_label(axes[1, 0].containers[0])
axes[1, 0].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 1], x=x[4], y=y[4])
axes[1, 1].set_title("Number of Fifth Categories used in Articles of K. N. Toosi University of Technology")
axes[1, 1].bar_label(axes[1, 1].containers[0])
axes[1, 1].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 2], x=x[5], y=y[5])
axes[1, 2].set_title("Number of Sixth Categories used in Articles of K. N. Toosi University of Technology")
axes[1, 2].bar_label(axes[1, 2].containers[0])
axes[1, 2].tick_params(labelrotation=90)

sns.barplot(ax=axes[2, 1], x=x[6], y=y[6])
axes[2, 1].set_title("Number of Seventh Categories used in Articles of K. N. Toosi University of Technology")
axes[2, 1].bar_label(axes[2, 1].containers[0])
axes[2, 1].tick_params(labelrotation=90)

fig.delaxes(axes[2, 0])
fig.delaxes(axes[2, 2])

fig.tight_layout()
plt.show()

Sharif University of Technology top 7 Categories

• Although labeled as the second best university in Iran, its research output is not in the second place, it's in third place after Tarbiat Modares University.
• As it was predictable, since this university is more focused on the technical sciences, the most published articles of this university, categorized into Engineering as their first major category.
• the Engineering category as first or second major one, has a significant distance from other categories.

fig, axes = plt.subplots(3, 3, figsize=(20, 20))
x = [
    list(df[df['University'] == "Sharif University of Technology"]['Category_1'].value_counts().index),
    list(df[df['University'] == "Sharif University of Technology"]['Category_2'].value_counts().index),
    list(df[df['University'] == "Sharif University of Technology"]['Category_3'].value_counts().index),
    list(df[df['University'] == "Sharif University of Technology"]['Category_4'].value_counts().index),
    list(df[df['University'] == "Sharif University of Technology"]['Category_5'].value_counts().index),
    list(df[df['University'] == "Sharif University of Technology"]['Category_6'].value_counts().index),
    list(df[df['University'] == "Sharif University of Technology"]['Category_7'].value_counts().index),

]
y = [
    list(df[df['University'] == "Sharif University of Technology"]['Category_1'].value_counts()),
    list(df[df['University'] == "Sharif University of Technology"]['Category_2'].value_counts()),
    list(df[df['University'] == "Sharif University of Technology"]['Category_3'].value_counts()),
    list(df[df['University'] == "Sharif University of Technology"]['Category_4'].value_counts()),
    list(df[df['University'] == "Sharif University of Technology"]['Category_5'].value_counts()),
    list(df[df['University'] == "Sharif University of Technology"]['Category_6'].value_counts()),
    list(df[df['University'] == "Sharif University of Technology"]['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0, 0], x=x[0], y=y[0])
axes[0, 0].set_title("Number of First Categories used in Articles of Sharif University of Technology")
axes[0, 0].bar_label(axes[0, 0].containers[0], rotation=90)
axes[0, 0].tick_params(labelrotation=90)

sns.barplot(ax=axes[0, 1], x=x[1], y=y[1])
axes[0, 1].set_title("Number of Second Categories used in Articles of Sharif University of Technology")
axes[0, 1].bar_label(axes[0, 1].containers[0], rotation=90)
axes[0, 1].tick_params(labelrotation=90)

sns.barplot(ax=axes[0, 2], x=x[2], y=y[2])
axes[0, 2].set_title("Number of Third Categories used in Articles of Sharif University of Technology")
axes[0, 2].bar_label(axes[0, 2].containers[0], rotation=90)
axes[0, 2].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 0], x=x[3], y=y[3])
axes[1, 0].set_title("Number of Fourth Categories used in Articles of Sharif University of Technology")
axes[1, 0].bar_label(axes[1, 0].containers[0])
axes[1, 0].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 1], x=x[4], y=y[4])
axes[1, 1].set_title("Number of Fifth Categories used in Articles of Sharif University of Technology")
axes[1, 1].bar_label(axes[1, 1].containers[0])
axes[1, 1].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 2], x=x[5], y=y[5])
axes[1, 2].set_title("Number of Sixth Categories used in Articles of Sharif University of Technology")
axes[1, 2].bar_label(axes[1, 2].containers[0])
axes[1, 2].tick_params(labelrotation=90)

sns.barplot(ax=axes[2, 1], x=x[6], y=y[6])
axes[2, 1].set_title("Number of Seventh Categories used in Articles of Sharif University of Technology")
axes[2, 1].bar_label(axes[2, 1].containers[0])
axes[2, 1].tick_params(labelrotation=90)

fig.delaxes(axes[2, 0])
fig.delaxes(axes[2, 2])

fig.tight_layout()
plt.show()

University of Shiraz Top 7 Categories

• Engineering is the top major cateogry for the published articles of this university.
• Biological Sciences, Medical and Health Sciences and Chemical Sciences are in the second place with almost similar amount.

fig, axes = plt.subplots(3, 3, figsize=(20, 20))
x = [
    list(df[df['University'] == "University of Shiraz"]['Category_1'].value_counts().index),
    list(df[df['University'] == "University of Shiraz"]['Category_2'].value_counts().index),
    list(df[df['University'] == "University of Shiraz"]['Category_3'].value_counts().index),
    list(df[df['University'] == "University of Shiraz"]['Category_4'].value_counts().index),
    list(df[df['University'] == "University of Shiraz"]['Category_5'].value_counts().index),
    list(df[df['University'] == "University of Shiraz"]['Category_6'].value_counts().index),
    list(df[df['University'] == "University of Shiraz"]['Category_7'].value_counts().index),

]
y = [
    list(df[df['University'] == "University of Shiraz"]['Category_1'].value_counts()),
    list(df[df['University'] == "University of Shiraz"]['Category_2'].value_counts()),
    list(df[df['University'] == "University of Shiraz"]['Category_3'].value_counts()),
    list(df[df['University'] == "University of Shiraz"]['Category_4'].value_counts()),
    list(df[df['University'] == "University of Shiraz"]['Category_5'].value_counts()),
    list(df[df['University'] == "University of Shiraz"]['Category_6'].value_counts()),
    list(df[df['University'] == "University of Shiraz"]['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0, 0], x=x[0], y=y[0])
axes[0, 0].set_title("Number of First Categories used in Articles of University of Shiraz")
axes[0, 0].bar_label(axes[0, 0].containers[0], rotation=90)
axes[0, 0].tick_params(labelrotation=90)

sns.barplot(ax=axes[0, 1], x=x[1], y=y[1])
axes[0, 1].set_title("Number of Second Categories used in Articles of University of Shiraz")
axes[0, 1].bar_label(axes[0, 1].containers[0], rotation=90)
axes[0, 1].tick_params(labelrotation=90)

sns.barplot(ax=axes[0, 2], x=x[2], y=y[2])
axes[0, 2].set_title("Number of Third Categories used in Articles of University of Shiraz")
axes[0, 2].bar_label(axes[0, 2].containers[0], rotation=90)
axes[0, 2].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 0], x=x[3], y=y[3])
axes[1, 0].set_title("Number of Fourth Categories used in Articles of University of Shiraz")
axes[1, 0].bar_label(axes[1, 0].containers[0])
axes[1, 0].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 1], x=x[4], y=y[4])
axes[1, 1].set_title("Number of Fifth Categories used in Articles of University of Shiraz")
axes[1, 1].bar_label(axes[1, 1].containers[0])
axes[1, 1].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 2], x=x[5], y=y[5])
axes[1, 2].set_title("Number of Sixth Categories used in Articles of University of Shiraz")
axes[1, 2].bar_label(axes[1, 2].containers[0])
axes[1, 2].tick_params(labelrotation=90)

sns.barplot(ax=axes[2, 1], x=x[6], y=y[6])
axes[2, 1].set_title("Number of Seventh Categories used in Articles of University of Shiraz")
axes[2, 1].bar_label(axes[2, 1].containers[0])
axes[2, 1].tick_params(labelrotation=90)

fig.delaxes(axes[2, 0])
fig.delaxes(axes[2, 2])

fig.tight_layout()
plt.show()

Iran University of Science and Technology Top 7 Categories

• As it was predictable, since this university is more focused on the technical sciences, the most published articles of this university, categorized into Engineering as their first major category.
• the Engineering category as first or second major one, has a significant distance from other categories.

fig, axes = plt.subplots(3, 3, figsize=(20, 20))
x = [
    list(df[df['University'] == "Iran University of Science and Technology"]['Category_1'].value_counts().index),
    list(df[df['University'] == "Iran University of Science and Technology"]['Category_2'].value_counts().index),
    list(df[df['University'] == "Iran University of Science and Technology"]['Category_3'].value_counts().index),
    list(df[df['University'] == "Iran University of Science and Technology"]['Category_4'].value_counts().index),
    list(df[df['University'] == "Iran University of Science and Technology"]['Category_5'].value_counts().index),
    list(df[df['University'] == "Iran University of Science and Technology"]['Category_6'].value_counts().index),
    list(df[df['University'] == "Iran University of Science and Technology"]['Category_7'].value_counts().index),

]
y = [
    list(df[df['University'] == "Iran University of Science and Technology"]['Category_1'].value_counts()),
    list(df[df['University'] == "Iran University of Science and Technology"]['Category_2'].value_counts()),
    list(df[df['University'] == "Iran University of Science and Technology"]['Category_3'].value_counts()),
    list(df[df['University'] == "Iran University of Science and Technology"]['Category_4'].value_counts()),
    list(df[df['University'] == "Iran University of Science and Technology"]['Category_5'].value_counts()),
    list(df[df['University'] == "Iran University of Science and Technology"]['Category_6'].value_counts()),
    list(df[df['University'] == "Iran University of Science and Technology"]['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0, 0], x=x[0], y=y[0])
axes[0, 0].set_title("Number of First Categories used in Articles of University of Science and Technology")
axes[0, 0].bar_label(axes[0, 0].containers[0], rotation=90)
axes[0, 0].tick_params(labelrotation=90)

sns.barplot(ax=axes[0, 1], x=x[1], y=y[1])
axes[0, 1].set_title("Number of Second Categories used in Articles of University of Science and Technology")
axes[0, 1].bar_label(axes[0, 1].containers[0], rotation=90)
axes[0, 1].tick_params(labelrotation=90)

sns.barplot(ax=axes[0, 2], x=x[2], y=y[2])
axes[0, 2].set_title("Number of Third Categories used in Articles of University of Science and Technology")
axes[0, 2].bar_label(axes[0, 2].containers[0], rotation=90)
axes[0, 2].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 0], x=x[3], y=y[3])
axes[1, 0].set_title("Number of Fourth Categories used in Articles of University of Science and Technology")
axes[1, 0].bar_label(axes[1, 0].containers[0])
axes[1, 0].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 1], x=x[4], y=y[4])
axes[1, 1].set_title("Number of Fifth Categories used in Articles of University of Science and Technology")
axes[1, 1].bar_label(axes[1, 1].containers[0])
axes[1, 1].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 2], x=x[5], y=y[5])
axes[1, 2].set_title("Number of Sixth Categories used in Articles of University of Science and Technology")
axes[1, 2].bar_label(axes[1, 2].containers[0])
axes[1, 2].tick_params(labelrotation=90)

sns.barplot(ax=axes[2, 1], x=x[6], y=y[6])
axes[2, 1].set_title("Number of Seventh Categories used in Articles of University of Science and Technology")
axes[2, 1].bar_label(axes[2, 1].containers[0])
axes[2, 1].tick_params(labelrotation=90)

fig.delaxes(axes[2, 0])
fig.delaxes(axes[2, 2])

fig.tight_layout()
plt.show()

University of Tabriz Top 7 Categories

• Engineering is the top major cateogry for the published articles of this university.

fig, axes = plt.subplots(3, 3, figsize=(20, 20))
x = [
    list(df[df['University'] == "University of Tabriz"]['Category_1'].value_counts().index),
    list(df[df['University'] == "University of Tabriz"]['Category_2'].value_counts().index),
    list(df[df['University'] == "University of Tabriz"]['Category_3'].value_counts().index),
    list(df[df['University'] == "University of Tabriz"]['Category_4'].value_counts().index),
    list(df[df['University'] == "University of Tabriz"]['Category_5'].value_counts().index),
    list(df[df['University'] == "University of Tabriz"]['Category_6'].value_counts().index),
    list(df[df['University'] == "University of Tabriz"]['Category_7'].value_counts().index),

]
y = [
    list(df[df['University'] == "University of Tabriz"]['Category_1'].value_counts()),
    list(df[df['University'] == "University of Tabriz"]['Category_2'].value_counts()),
    list(df[df['University'] == "University of Tabriz"]['Category_3'].value_counts()),
    list(df[df['University'] == "University of Tabriz"]['Category_4'].value_counts()),
    list(df[df['University'] == "University of Tabriz"]['Category_5'].value_counts()),
    list(df[df['University'] == "University of Tabriz"]['Category_6'].value_counts()),
    list(df[df['University'] == "University of Tabriz"]['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0, 0], x=x[0], y=y[0])
axes[0, 0].set_title("Number of First Categories used in Articles of University of Tabriz")
axes[0, 0].bar_label(axes[0, 0].containers[0], rotation=90)
axes[0, 0].tick_params(labelrotation=90)

sns.barplot(ax=axes[0, 1], x=x[1], y=y[1])
axes[0, 1].set_title("Number of Second Categories used in Articles of University of Tabriz")
axes[0, 1].bar_label(axes[0, 1].containers[0], rotation=90)
axes[0, 1].tick_params(labelrotation=90)

sns.barplot(ax=axes[0, 2], x=x[2], y=y[2])
axes[0, 2].set_title("Number of Third Categories used in Articles of University of Tabriz")
axes[0, 2].bar_label(axes[0, 2].containers[0], rotation=90)
axes[0, 2].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 0], x=x[3], y=y[3])
axes[1, 0].set_title("Number of Fourth Categories used in Articles of University of Tabriz")
axes[1, 0].bar_label(axes[1, 0].containers[0])
axes[1, 0].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 1], x=x[4], y=y[4])
axes[1, 1].set_title("Number of Fifth Categories used in Articles of University of Tabriz")
axes[1, 1].bar_label(axes[1, 1].containers[0])
axes[1, 1].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 2], x=x[5], y=y[5])
axes[1, 2].set_title("Number of Sixth Categories used in Articles of University of Tabriz")
axes[1, 2].bar_label(axes[1, 2].containers[0])
axes[1, 2].tick_params(labelrotation=90)

sns.barplot(ax=axes[2, 1], x=x[6], y=y[6])
axes[2, 1].set_title("Number of Seventh Categories used in Articles of University of Tabriz")
axes[2, 1].bar_label(axes[2, 1].containers[0])
axes[2, 1].tick_params(labelrotation=90)

fig.delaxes(axes[2, 0])
fig.delaxes(axes[2, 2])

fig.tight_layout()
plt.show()

Tarbiat Modares University Top 7 Categories

• This university has the second most published articles among the investigated universities.
• Unlike the majority of other universities, Medical and Health Sciences is the most used category as the first major category of published articles of this university.

fig, axes = plt.subplots(3, 3, figsize=(20, 20))
x = [
    list(df[df['University'] == "Tarbiat Modares University"]['Category_1'].value_counts().index),
    list(df[df['University'] == "Tarbiat Modares University"]['Category_2'].value_counts().index),
    list(df[df['University'] == "Tarbiat Modares University"]['Category_3'].value_counts().index),
    list(df[df['University'] == "Tarbiat Modares University"]['Category_4'].value_counts().index),
    list(df[df['University'] == "Tarbiat Modares University"]['Category_5'].value_counts().index),
    list(df[df['University'] == "Tarbiat Modares University"]['Category_6'].value_counts().index),
    list(df[df['University'] == "Tarbiat Modares University"]['Category_7'].value_counts().index),

]
y = [
    list(df[df['University'] == "Tarbiat Modares University"]['Category_1'].value_counts()),
    list(df[df['University'] == "Tarbiat Modares University"]['Category_2'].value_counts()),
    list(df[df['University'] == "Tarbiat Modares University"]['Category_3'].value_counts()),
    list(df[df['University'] == "Tarbiat Modares University"]['Category_4'].value_counts()),
    list(df[df['University'] == "Tarbiat Modares University"]['Category_5'].value_counts()),
    list(df[df['University'] == "Tarbiat Modares University"]['Category_6'].value_counts()),
    list(df[df['University'] == "Tarbiat Modares University"]['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0, 0], x=x[0], y=y[0])
axes[0, 0].set_title("Number of First Categories used in Articles of Tarbiat Modares University")
axes[0, 0].bar_label(axes[0, 0].containers[0], rotation=90)
axes[0, 0].tick_params(labelrotation=90)

sns.barplot(ax=axes[0, 1], x=x[1], y=y[1])
axes[0, 1].set_title("Number of Second Categories used in Articles of Tarbiat Modares University")
axes[0, 1].bar_label(axes[0, 1].containers[0], rotation=90)
axes[0, 1].tick_params(labelrotation=90)

sns.barplot(ax=axes[0, 2], x=x[2], y=y[2])
axes[0, 2].set_title("Number of Third Categories used in Articles of Tarbiat Modares University")
axes[0, 2].bar_label(axes[0, 2].containers[0], rotation=90)
axes[0, 2].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 0], x=x[3], y=y[3])
axes[1, 0].set_title("Number of Fourth Categories used in Articles of Tarbiat Modares University")
axes[1, 0].bar_label(axes[1, 0].containers[0])
axes[1, 0].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 1], x=x[4], y=y[4])
axes[1, 1].set_title("Number of Fifth Categories used in Articles of Tarbiat Modares University")
axes[1, 1].bar_label(axes[1, 1].containers[0])
axes[1, 1].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 2], x=x[5], y=y[5])
axes[1, 2].set_title("Number of Sixth Categories used in Articles of Tarbiat Modares University")
axes[1, 2].bar_label(axes[1, 2].containers[0])
axes[1, 2].tick_params(labelrotation=90)

sns.barplot(ax=axes[2, 1], x=x[6], y=y[6])
axes[2, 1].set_title("Number of Seventh Categories used in Articles of Tarbiat Modares University")
axes[2, 1].bar_label(axes[2, 1].containers[0])
axes[2, 1].tick_params(labelrotation=90)

fig.delaxes(axes[2, 0])
fig.delaxes(axes[2, 2])

fig.tight_layout()
plt.show()

University of Tehran Top 7 Categories

• This university has the most published articles among the universities and ranked as the top university of Iran.
• Although the most used first major of this university published articles is Engineering, for each speciliazied categories such as human sciences and etc, still has the most published articles in contrast of other niche focused universities.

fig, axes = plt.subplots(3, 3, figsize=(20, 20))
x = [
    list(df[df['University'] == "University of Tehran"]['Category_1'].value_counts().index),
    list(df[df['University'] == "University of Tehran"]['Category_2'].value_counts().index),
    list(df[df['University'] == "University of Tehran"]['Category_3'].value_counts().index),
    list(df[df['University'] == "University of Tehran"]['Category_4'].value_counts().index),
    list(df[df['University'] == "University of Tehran"]['Category_5'].value_counts().index),
    list(df[df['University'] == "University of Tehran"]['Category_6'].value_counts().index),
    list(df[df['University'] == "University of Tehran"]['Category_7'].value_counts().index),

]
y = [
    list(df[df['University'] == "University of Tehran"]['Category_1'].value_counts()),
    list(df[df['University'] == "University of Tehran"]['Category_2'].value_counts()),
    list(df[df['University'] == "University of Tehran"]['Category_3'].value_counts()),
    list(df[df['University'] == "University of Tehran"]['Category_4'].value_counts()),
    list(df[df['University'] == "University of Tehran"]['Category_5'].value_counts()),
    list(df[df['University'] == "University of Tehran"]['Category_6'].value_counts()),
    list(df[df['University'] == "University of Tehran"]['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0, 0], x=x[0], y=y[0])
axes[0, 0].set_title("Number of First Categories used in Articles of University of Tehran")
axes[0, 0].bar_label(axes[0, 0].containers[0], rotation=90)
axes[0, 0].tick_params(labelrotation=90)

sns.barplot(ax=axes[0, 1], x=x[1], y=y[1])
axes[0, 1].set_title("Number of Second Categories used in Articles of University of Tehran")
axes[0, 1].bar_label(axes[0, 1].containers[0], rotation=90)
axes[0, 1].tick_params(labelrotation=90)

sns.barplot(ax=axes[0, 2], x=x[2], y=y[2])
axes[0, 2].set_title("Number of Third Categories used in Articles of University of Tehran")
axes[0, 2].bar_label(axes[0, 2].containers[0], rotation=90)
axes[0, 2].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 0], x=x[3], y=y[3])
axes[1, 0].set_title("Number of Fourth Categories used in Articles of University of Tehran")
axes[1, 0].bar_label(axes[1, 0].containers[0])
axes[1, 0].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 1], x=x[4], y=y[4])
axes[1, 1].set_title("Number of Fifth Categories used in Articles of University of Tehran")
axes[1, 1].bar_label(axes[1, 1].containers[0])
axes[1, 1].tick_params(labelrotation=90)

sns.barplot(ax=axes[1, 2], x=x[5], y=y[5])
axes[1, 2].set_title("Number of Sixth Categories used in Articles of University of Tehran")
axes[1, 2].bar_label(axes[1, 2].containers[0])
axes[1, 2].tick_params(labelrotation=90)

sns.barplot(ax=axes[2, 1], x=x[6], y=y[6])
axes[2, 1].set_title("Number of Seventh Categories used in Articles of University of Tehran")
axes[2, 1].bar_label(axes[2, 1].containers[0])
axes[2, 1].tick_params(labelrotation=90)

fig.delaxes(axes[2, 0])
fig.delaxes(axes[2, 2])

fig.tight_layout()
plt.show()

Research Output Type and Open Acess situation

• Most of the research outputs tracked in this dataset (92.2%) is article, 7.6% is chapter, 98 books and 1 news also monitored.
• Most of the published research outputs (65%) is not under open access policies.
• Out of the outputs published under open access policies, 44% of them published in Gold open access journals, 37.4% in Green open access journals, 10.2% in Bronze open access journals and 7.6% in Hybrid open access journals.

fig, axes = plt.subplots(1, 3, figsize=(10, 5))
x = [
    list(df['Output_Type'].value_counts().index),
    list(df['OA_Status'].value_counts().index),
    list(df['OA_Type'].value_counts().index),
]
y = [
    list(df['Output_Type'].value_counts()),
    list(df['OA_Status'].value_counts()),
    list(df['OA_Type'].value_counts()),

]
sns.barplot(ax=axes[0], x=x[0], y=y[0])
axes[0].set_title('Research Output Type')
axes[0].bar_label(axes[0].containers[0])
axes[0].tick_params(labelrotation=90)

sns.barplot(ax=axes[1], x=x[1], y=y[1])
axes[1].set_title('Open Access Status')
axes[1].bar_label(axes[1].containers[0])
axes[1].tick_params(labelrotation=90)

sns.barplot(ax=axes[2], x=x[2], y=y[2])
axes[2].set_title('Open Access Type')
axes[2].bar_label(axes[2].containers[0])
axes[2].tick_params(labelrotation=90)


plt.show()

Publication Trend

In the plot below you can see the trend of publication based on the date. It's obvious that the trend is growing and more research output is generated. This trait might be due to the increase of tracking research outputs of investigated universities.

plt.figure(figsize=(15, 5))
ax = sns.histplot(data=df.loc[df['Publication_Date'] >= '2000'], x='Publication_Date', kde=True)
ax.bar_label(ax.containers[0])
plt.title('The Number of published articles after 2000')
plt.show()

In the next cell you can the statistical description of each features.

df.describe()

	Altmetric_Attention_Score	News_mentions	Blog_mentions	Policy_mentions	Patent_mentions	Twitter_mentions	Peer_review_mentions	Weibo_mentions	Facebook_mentions	Wikipedia_mentions	Google+_mentions	LinkedIn_mentions	Reddit_mentions	Pinterest_mentions	F1000_mentions	Q&A_mentions	Video_mentions	Number_of_Mendeley_readers	Number_of_Dimensions_citations	Journal/Collection_Title_LE	Output_Type_LE	OA_Status_LE	OA_Type_LE	Publisher_Names_LE	University_LE	Category_1_LE	Category_2_LE	Category_3_LE	Category_4_LE	Category_5_LE	Category_6_LE	Category_7_LE
count	74009.000000	74009.000000	74009.000000	74009.000000	74009.000000	74009.000000	74009.000000	74009.000000	74009.000000	74009.000000	74009.000000	74009.000000	74009.000000	74009.000000	74009.000000	74009.000000	74009.000000	74009.000000	74009.000000	74009.000000	74009.000000	74009.000000	74009.000000	74009.000000	74009.000000	74009.000000	74009.000000	74009.000000	74009.000000	74009.000000	74009.000000	74009.000000
mean	3.067195	0.138929	0.039576	0.018228	0.161872	2.469929	0.020822	0.000189	0.081828	0.071640	0.009904	0.000014	0.007526	0.000041	0.000919	0.001040	0.008594	34.362929	21.984380	4554.856396	0.153941	0.349322	1.462133	114.151536	8.645057	7.200935	26.060047	9.685403	1.418706	0.203137	0.029051	0.000041
std	33.615673	2.703754	0.400570	0.190594	1.664764	46.029952	0.289693	0.022659	0.766598	1.365636	0.243656	0.003676	0.121293	0.006367	0.035627	0.033874	0.168230	68.950118	59.464531	2240.940423	0.531887	0.476759	0.862054	50.320941	4.359108	4.228762	9.891273	14.353519	4.742279	1.642100	0.519035	0.006367
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	6.000000	2.000000	2606.000000	0.000000	0.000000	1.000000	78.000000	5.000000	3.000000	23.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	16.000000	9.000000	4876.000000	0.000000	0.000000	1.000000	98.000000	9.000000	8.000000	26.000000	0.000000	0.000000	0.000000	0.000000	0.000000
75%	2.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	37.000000	24.000000	6346.000000	0.000000	1.000000	2.000000	171.000000	13.000000	9.000000	32.000000	22.000000	0.000000	0.000000	0.000000	0.000000
max	4568.000000	290.000000	34.000000	12.000000	248.000000	8949.000000	19.000000	4.000000	109.000000	227.000000	34.000000	1.000000	11.000000	1.000000	4.000000	2.000000	17.000000	2915.000000	8264.000000	8192.000000	3.000000	1.000000	4.000000	189.000000	14.000000	42.000000	43.000000	41.000000	25.000000	20.000000	13.000000	1.000000

In the next cell we are going to visualize a scatter plot for each feature pair. Since we have a good amount of features in the dataset, this plot will be huge. To mitigate this problem, we will remove features with less than 0.25 standard deviation. The Standard Deviation Values are presented in the table above.

target_features = list(df.describe().columns[:-13])
temp = []
for feature in target_features:
    if df.describe()[feature]['std'] >= 0.25:
        temp.append(feature)
target_features = temp
axes = pd.plotting.scatter_matrix(df[target_features], figsize=(50, 30), s=100)
for ax in axes.flatten():
    ax.xaxis.label.set_rotation(90)
    ax.yaxis.label.set_rotation(0)
    ax.yaxis.label.set_ha('right')
plt.show()

Correlation Analysis

In the next cell we try to explore and analyze the correlation status of features in the dataframe. Have in mind that correlation is meaningless for categorical values, thus we have to make these type of values label encoded. have in mind we will only label encode values that we believe will result in meaningful result.

• There is strong correlation between the Altmetric Attention Score, and News Mentions, Blog Mentions and Twitter Mentions. This trait should be further investigated, since we saw on the described statistical features of columns that these features attract the most of the mentions among all type of mentions.
• There are no significance correlation between features, except the two features of Open Access type and status since they are perfectly related.

intercor = df.corr(numeric_only=True)
plt.figure(figsize=(25,25))
sns.heatmap(intercor,annot=True, cmap='rocket_r', fmt='.3f')
plt.tight_layout()
plt.title('Features Correlation Heat Map')
plt.show()

Grouping Features Analysis

In the cells below we will explore the data by grouping features and calculate the aggregations.

df.groupby('Output_Type')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std'])

	count	mean	max	min	std
Output_Type
Article	68264	3.302854	4568	0	34.969362
Book	98	1.204082	27	0	3.220294
Chapter	5646	0.246192	220	0	4.284104
News	1	26.000000	26	26	NaN

df.groupby('OA_Status')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std'])

	count	mean	max	min	std
OA_Status
False	48156	1.855117	790	0	9.528782
True	25853	5.324914	4568	0	55.299093

An interesting insight that we can deduce from the table above, is that publishing under open access policies will result in better performing and more mentions. Although the number of articles under open access policies are lower that closed ones, they resulted in more mentions on average than the closed ones.

df.groupby('OA_Type')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std'])

	count	mean	max	min	std
OA_Type
bronze	2656	9.338479	4568	0	116.910033
closed	48155	1.855155	790	0	9.528877
gold	11523	4.090167	1410	0	22.571000
green	9690	4.310630	2766	0	47.675711
hybrid	1985	12.071033	1502	0	86.116285

df.groupby('Publisher_Names')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)

	count	mean	max	min	std
Publisher_Names
Elsevier BV	21585	2.279129	696	0	11.427640
Springer Nature	13776	1.528818	1017	0	10.665141
Institute of Electrical and Electronics Engineers (IEEE)	4148	1.224446	95	0	3.094993
ESSOAr; Natural History Museum; Wiley	3991	2.306439	426	0	11.489564
GeoScienceWorld; Taylor & Francis	3633	2.218277	282	0	10.420666
...	...	...	...	...	...
Oxford University Press (OUP); Taylor & Francis	1	0.000000	0	0	NaN
American Diabetes Association	1	6.000000	6	6	NaN
Hindawi Limited; Springer Nature	1	3.000000	3	3	NaN
Consortium Erudit; GeoScienceWorld; Taylor & Francis	1	3.000000	3	3	NaN
American Thoracic Society; StatRef	1	1.000000	1	1	NaN

189 rows × 5 columns

df.groupby('University')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)

	count	mean	max	min	std
University
University of Tehran	16194	3.995863	4568	0	56.224681
Tarbiat Modares University	8870	3.917249	2043	0	35.927520
Sharif University of Technology	6866	2.918730	1367	0	23.913502
Amir Kabir University	5753	1.788632	1367	0	22.824887
University of Shiraz	5572	2.689698	633	0	15.228685
Ferdowsi University of Mashhad	4910	3.114053	1017	0	21.330601
Shahid Beheshti University	4793	3.412477	1410	0	27.233875
Isfahan University of Technology	4724	2.270957	372	0	9.669328
Iran University of Science and Technology	4356	1.435262	507	0	9.761763
University of Tabriz	4130	1.948184	349	0	8.935398
University of Isfahan	3103	3.471157	623	0	19.017597
K. N. Toosi University of Technology	2103	1.725630	221	0	7.409996
University of Guilan	2049	3.160566	790	0	21.222986
Allameh Tabataba'i University	461	9.624729	1629	0	88.069070
Imam Hossein University	125	1.944000	25	0	3.046161

It's worth to mention that, Although Allameh Tabataba'i University has the second to last amount of research output, it has the highest amount of Altmetric attention score on average. It's standard deviation is also highest among the universities. This fact indicates an outlier among the publications of this university.

df.groupby('Category_1')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)

	count	mean	max	min	std
Category_1
09 Engineering	16476	1.444829	661	0	8.146969
11 Medical and Health Sciences	10732	6.795471	4568	0	61.368467
03 Chemical Sciences	10042	1.832503	201	0	4.575673
06 Biological Sciences	8161	4.476045	2766	0	42.644529
08 Information and Computing Sciences	6783	1.423411	1017	0	13.187518
01 Mathematical Sciences	5183	1.368705	190	0	5.173754
02 Physical Sciences	4605	2.786102	329	0	10.989553
04 Earth Sciences	2807	4.155682	3149	0	61.895835
05 Environmental Sciences	1785	4.882913	961	0	33.274107
07 Agricultural and Veterinary Sciences	1213	1.930750	202	0	6.806992
17 Psychology and Cognitive Sciences	1167	5.036847	333	0	20.024843
10 Technology	1146	1.419721	140	0	5.251002
15 Commerce, Management, Tourism and Services	699	1.589413	141	0	7.767499
14 Economics	636	2.962264	409	0	19.583904
16 Studies in Human Society	590	3.288136	175	0	11.463755
13 Education	427	2.405152	66	0	6.048107
00 No Category	383	0.950392	69	0	4.294785
12 Built Environment and Design	333	1.531532	36	0	3.895054
20 Language, Communication and Culture	203	2.004926	31	0	4.133296
21 History and Archaeology	138	51.557971	2043	0	281.978050
22 Philosophy and Religious Studies	121	2.776860	60	0	7.356955
40 Engineering	82	0.792683	10	0	1.810380
32 Biomedical and Clinical Sciences	56	1.375000	23	0	3.887334
18 Law and Legal Studies	43	6.627907	56	0	13.569966
31 Biological Sciences	39	1.923077	16	0	3.055492
46 Information and Computing Sciences	25	1.040000	11	0	2.406242
49 Mathematical Sciences	20	0.150000	2	0	0.489360
34 Chemical Sciences	17	1.764706	8	0	1.953504
19 Studies in Creative Arts and Writing	16	2.437500	10	0	3.182635
37 Earth Sciences	14	0.714286	3	0	1.069045
35 Commerce, Management, Tourism and Services	13	1.153846	3	0	1.143544
44 Human Society	8	1.750000	3	1	0.886405
30 Agricultural, Veterinary and Food Sciences	8	2.000000	9	0	3.023716
50 Philosophy and Religious Studies	8	17.250000	66	0	30.103393
33 Built Environment and Design	6	1.166667	2	0	0.752773
51 Physical Sciences	6	2.166667	9	0	3.488075
52 Psychology	5	2.200000	7	1	2.683282
42 Health Sciences	3	1.000000	1	1	0.000000
41 Environmental Sciences	3	0.666667	1	0	0.577350
47 Language, Communication and Culture	2	0.000000	0	0	0.000000
39 Education	2	2.000000	3	1	1.414214
38 Economics	2	0.500000	1	0	0.707107
36 Creative Arts and Writing	1	0.000000	0	0	NaN

As it was discussed earlier, the majority of research output had Engineering as their category. But the majority of attentions was retrieved by the research output which had Medical and Health Sciences as their first category. This trait is probably due to the Covid-19 pandemic and the fact that research about this pandemic retrieved much more attention in the media.

Another interesting insight, is the fact that some research outputs in category of 21 History and Archaeology attract some attention, after further investigation, it was concluded that there is a research output with about 2000 attention score that was both credited to University of Tehran and Tarbiat Modares University.

df.groupby('Category_2')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)

	count	mean	max	min	std
Category_2
40 Engineering	17490	1.260835	369	0	4.751832
32 Biomedical and Clinical Sciences	7645	6.060562	4568	0	60.612184
34 Chemical Sciences	7452	1.785293	182	0	4.002456
31 Biological Sciences	6882	4.774920	1629	0	36.021628
46 Information and Computing Sciences	4580	1.340175	1017	0	15.497656
09 Engineering	3873	1.709786	191	0	5.868211
30 Agricultural, Veterinary and Food Sciences	3487	3.680528	2043	0	55.416739
37 Earth Sciences	2817	4.046858	3149	0	62.551944
51 Physical Sciences	2771	3.561891	507	0	17.742544
49 Mathematical Sciences	2163	1.509940	45	0	2.946684
42 Health Sciences	1416	9.480932	1367	0	70.732720
35 Commerce, Management, Tourism and Services	1243	1.448914	179	0	8.003598
11 Medical and Health Sciences	1061	3.835061	948	0	29.741752
06 Biological Sciences	939	6.604899	961	0	43.754115
33 Built Environment and Design	895	2.879330	661	0	25.183465
02 Physical Sciences	826	2.829298	102	0	8.106483
10 Technology	771	1.688716	28	0	2.876978
41 Environmental Sciences	764	3.738220	897	0	33.659922
07 Agricultural and Veterinary Sciences	738	1.897019	49	0	3.412837
17 Psychology and Cognitive Sciences	734	6.359673	585	0	32.664048
08 Information and Computing Sciences	677	1.109306	37	0	2.475296
52 Psychology	654	7.568807	471	0	32.072668
00 No Category	582	1.130584	69	0	3.879439
05 Environmental Sciences	397	2.617128	215	0	11.783806
44 Human Society	375	3.765333	111	0	10.423563
03 Chemical Sciences	342	3.017544	134	0	10.337685
38 Economics	319	2.344828	119	0	9.500425
39 Education	317	2.372240	44	0	5.253713
12 Built Environment and Design	292	3.071918	243	0	17.173812
15 Commerce, Management, Tourism and Services	260	1.334615	61	0	5.533293
16 Studies in Human Society	178	3.949438	66	0	9.710075
14 Economics	174	1.689655	31	0	3.868684
47 Language, Communication and Culture	163	2.036810	24	0	3.656365
04 Earth Sciences	125	2.120000	16	0	2.894934
50 Philosophy and Religious Studies	117	3.470085	60	0	8.375429
43 History, Heritage and Archaeology	104	5.644231	88	0	14.084294
20 Language, Communication and Culture	103	1.611650	22	0	2.762611
21 History and Archaeology	60	91.933333	2766	0	374.473681
36 Creative Arts and Writing	48	2.604167	31	0	4.832248
48 Law and Legal Studies	41	6.731707	56	0	13.881686
22 Philosophy and Religious Studies	38	6.657895	175	0	28.223314
13 Education	37	2.000000	13	0	3.291403
18 Law and Legal Studies	31	1.741935	9	0	2.780500
19 Studies in Creative Arts and Writing	28	1.642857	8	0	1.591977

df.groupby('Category_3')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)

	count	mean	max	min	std
Category_3
00 No Category	48454	2.858773	4568	0	31.506775
40 Engineering	5978	1.650719	201	0	5.741645
46 Information and Computing Sciences	2977	1.425596	357	0	8.054330
51 Physical Sciences	2184	2.132784	173	0	6.222088
31 Biological Sciences	1932	10.700311	2766	0	106.481177
32 Biomedical and Clinical Sciences	1753	5.353679	1629	0	48.307701
34 Chemical Sciences	1293	2.003094	134	0	5.454852
49 Mathematical Sciences	1134	1.514109	59	0	4.306369
42 Health Sciences	976	8.141393	409	0	28.374031
30 Agricultural, Veterinary and Food Sciences	950	1.887368	110	0	4.892625
41 Environmental Sciences	947	3.059134	430	0	19.006249
09 Engineering	678	2.246313	102	0	8.314699
52 Psychology	673	7.595840	288	0	26.560243
37 Earth Sciences	653	2.540582	215	0	10.938911
10 Technology	611	2.217676	191	0	8.242836
44 Human Society	387	14.953488	1367	0	120.638856
35 Commerce, Management, Tourism and Services	301	1.631229	61	0	6.369737
33 Built Environment and Design	278	2.672662	243	0	14.945534
11 Medical and Health Sciences	275	3.047273	191	0	11.871327
06 Biological Sciences	175	2.411429	55	0	6.605630
38 Economics	173	5.086705	661	0	50.224363
47 Language, Communication and Culture	166	2.584337	42	0	4.666008
15 Commerce, Management, Tourism and Services	154	1.461039	109	0	8.872468
17 Psychology and Cognitive Sciences	133	1.827068	44	0	4.484842
07 Agricultural and Veterinary Sciences	111	1.270270	17	0	2.276145
39 Education	110	1.854545	15	0	2.573101
16 Studies in Human Society	104	2.528846	31	0	5.307911
50 Philosophy and Religious Studies	83	3.807229	175	0	19.158809
08 Information and Computing Sciences	74	2.175676	13	0	2.953327
48 Law and Legal Studies	72	2.777778	52	0	7.027556
43 History, Heritage and Archaeology	55	26.854545	792	0	115.114342
20 Language, Communication and Culture	34	0.970588	8	0	1.660327
12 Built Environment and Design	25	1.960000	12	0	3.034249
03 Chemical Sciences	22	0.772727	3	0	0.869144
36 Creative Arts and Writing	15	1.066667	8	0	2.086236
22 Philosophy and Religious Studies	13	2.076923	9	0	2.531848
05 Environmental Sciences	13	0.615385	2	0	0.650444
18 Law and Legal Studies	12	1.750000	4	0	1.138180
14 Economics	10	0.900000	6	0	1.852926
19 Studies in Creative Arts and Writing	10	2.000000	6	0	1.943651
13 Education	6	5.333333	19	0	7.201852
21 History and Archaeology	5	1.800000	6	0	2.387467

df.groupby('Category_4')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)

	count	mean	max	min	std
Category_4
00 No Category	66832	2.879938	4568	0	30.058128
40 Engineering	1883	1.771110	82	0	3.946935
46 Information and Computing Sciences	797	1.908407	227	0	13.306888
51 Physical Sciences	785	2.719745	74	0	6.716408
41 Environmental Sciences	643	9.191291	961	0	58.319715
31 Biological Sciences	616	2.217532	110	0	5.558974
32 Biomedical and Clinical Sciences	414	2.748792	63	0	5.567954
34 Chemical Sciences	316	2.984177	191	0	12.253787
49 Mathematical Sciences	290	2.000000	102	0	10.691005
52 Psychology	272	9.904412	585	0	48.404870
30 Agricultural, Veterinary and Food Sciences	222	2.072072	54	0	4.724465
35 Commerce, Management, Tourism and Services	179	1.636872	109	0	8.332081
37 Earth Sciences	164	3.756098	241	0	19.546742
44 Human Society	154	9.928571	792	0	66.844773
42 Health Sciences	129	4.449612	59	0	8.645773
47 Language, Communication and Culture	69	3.855072	66	0	11.210913
33 Built Environment and Design	59	1.101695	11	0	2.179014
38 Economics	50	3.260000	108	0	15.168186
48 Law and Legal Studies	37	4.837838	108	0	17.659611
36 Creative Arts and Writing	29	1.931034	8	0	1.869512
43 History, Heritage and Archaeology	24	442.541667	2766	0	797.265707
50 Philosophy and Religious Studies	21	1.571429	15	0	3.264528
39 Education	18	2.277778	12	0	3.922867
10 Technology	3	0.666667	1	0	0.577350
09 Engineering	2	1.000000	1	1	0.000000
11 Medical and Health Sciences	1	6.000000	6	6	NaN

df.groupby('Category_5')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)

	count	mean	max	min	std
Category_5
00 No Category	72667	3.037761	4568	0	32.307099
40 Engineering	331	2.700906	191	0	11.599816
41 Environmental Sciences	204	2.049020	54	0	5.236058
46 Information and Computing Sciences	177	1.468927	23	0	2.569576
51 Physical Sciences	145	4.172414	102	0	14.840516
49 Mathematical Sciences	95	1.684211	21	0	3.498520
34 Chemical Sciences	82	2.036585	24	0	3.469248
32 Biomedical and Clinical Sciences	64	1.703125	11	0	2.044580
52 Psychology	41	4.780488	27	0	5.667946
44 Human Society	38	75.605263	2766	0	448.337924
31 Biological Sciences	36	1.055556	5	0	1.119807
42 Health Sciences	33	4.151515	44	0	8.039439
38 Economics	24	2.416667	11	0	3.525271
47 Language, Communication and Culture	17	1.235294	8	0	2.136861
37 Earth Sciences	13	2.000000	6	0	2.198484
35 Commerce, Management, Tourism and Services	12	0.333333	2	0	0.651339
48 Law and Legal Studies	10	1.000000	3	0	1.154701
50 Philosophy and Religious Studies	9	1.000000	5	0	1.581139
33 Built Environment and Design	4	1.750000	6	0	2.872281
43 History, Heritage and Archaeology	4	64.500000	241	0	117.854430
36 Creative Arts and Writing	3	1.666667	5	0	2.886751

df.groupby('Category_6')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)

	count	mean	max	min	std
Category_6
00 No Category	73746	3.070865	4568	0	33.674597
41 Environmental Sciences	61	2.180328	24	0	3.761685
51 Physical Sciences	41	2.536585	15	0	3.795376
44 Human Society	32	2.750000	22	0	4.898979
40 Engineering	27	1.296296	10	0	2.127021
46 Information and Computing Sciences	27	1.518519	10	0	2.562606
49 Mathematical Sciences	22	0.454545	2	0	0.670982
52 Psychology	19	3.789474	44	0	10.003216
42 Health Sciences	12	1.416667	4	1	0.900337
36 Creative Arts and Writing	9	2.555556	6	0	2.297341
32 Biomedical and Clinical Sciences	4	2.000000	3	1	0.816497
47 Language, Communication and Culture	4	0.500000	1	0	0.577350
34 Chemical Sciences	3	0.333333	1	0	0.577350
38 Economics	2	1.000000	1	1	0.000000

df.groupby('Category_7')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)

	count	mean	max	min	std
Category_7
00 No Category	74006	3.067238	4568	0	33.616353
49 Mathematical Sciences	3	2.000000	3	0	1.732051

Group by Top Category for each University

Allameh Tabataba'i University

df[df['University'] == "Allameh Tabataba'i University"].groupby('Category_1')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)

	count	mean	max	min	std
Category_1
11 Medical and Health Sciences	116	29.724138	1629	0	173.372511
17 Psychology and Cognitive Sciences	58	3.172414	31	0	5.959330
08 Information and Computing Sciences	45	1.311111	13	0	2.475537
01 Mathematical Sciences	39	1.717949	40	0	6.353402
15 Commerce, Management, Tourism and Services	39	3.435897	97	0	15.449059
13 Education	34	3.088235	66	0	11.163706
16 Studies in Human Society	29	7.965517	175	0	32.206347
09 Engineering	20	0.950000	4	0	1.276302
14 Economics	20	2.700000	16	0	4.612340
20 Language, Communication and Culture	19	2.578947	24	0	5.610662
12 Built Environment and Design	7	1.000000	3	0	1.000000
03 Chemical Sciences	6	1.000000	3	0	1.264911
21 History and Archaeology	5	1.200000	4	0	1.643168
22 Philosophy and Religious Studies	4	1.000000	3	0	1.414214
06 Biological Sciences	4	2.250000	6	0	2.629956
18 Law and Legal Studies	4	7.750000	18	0	7.500000
00 No Category	3	0.000000	0	0	0.000000
10 Technology	3	4.000000	9	0	4.582576
19 Studies in Creative Arts and Writing	1	0.000000	0	0	NaN
07 Agricultural and Veterinary Sciences	1	3.000000	3	3	NaN
05 Environmental Sciences	1	4.000000	4	4	NaN
02 Physical Sciences	1	3.000000	3	3	NaN
35 Commerce, Management, Tourism and Services	1	1.000000	1	1	NaN
46 Information and Computing Sciences	1	1.000000	1	1	NaN

Amir Kabir University

df[df['University'] == "Amir Kabir University"].groupby('Category_1')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)

	count	mean	max	min	std
Category_1
09 Engineering	2208	1.057518	28	0	1.986554
08 Information and Computing Sciences	816	1.182598	104	0	4.861744
03 Chemical Sciences	791	1.656131	106	0	4.291951
01 Mathematical Sciences	583	0.910806	59	0	2.841567
11 Medical and Health Sciences	390	9.964103	1367	0	86.568716
02 Physical Sciences	257	1.466926	12	0	1.932422
06 Biological Sciences	137	1.956204	34	0	3.886002
04 Earth Sciences	132	1.416667	26	0	2.887302
10 Technology	117	0.923077	7	0	1.457169
00 No Category	60	0.316667	6	0	1.065510
15 Commerce, Management, Tourism and Services	50	0.580000	5	0	1.070762
05 Environmental Sciences	43	1.511628	22	0	3.500830
17 Psychology and Cognitive Sciences	41	1.292683	7	0	1.569138
14 Economics	31	1.096774	9	0	2.134711
12 Built Environment and Design	19	1.157895	3	0	1.067872
16 Studies in Human Society	17	1.411765	15	0	3.742640
13 Education	12	1.666667	3	0	1.073087
22 Philosophy and Religious Studies	9	0.888889	3	0	0.927961
32 Biomedical and Clinical Sciences	7	1.142857	8	0	3.023716
40 Engineering	6	0.000000	0	0	0.000000
07 Agricultural and Veterinary Sciences	6	2.500000	9	0	3.728270
49 Mathematical Sciences	4	0.500000	2	0	1.000000
21 History and Archaeology	3	0.000000	0	0	0.000000
35 Commerce, Management, Tourism and Services	3	0.000000	0	0	0.000000
46 Information and Computing Sciences	3	3.666667	11	0	6.350853
20 Language, Communication and Culture	2	1.500000	3	0	2.121320
18 Law and Legal Studies	2	1.000000	1	1	0.000000
34 Chemical Sciences	1	8.000000	8	8	NaN
37 Earth Sciences	1	0.000000	0	0	NaN
47 Language, Communication and Culture	1	0.000000	0	0	NaN
50 Philosophy and Religious Studies	1	0.000000	0	0	NaN

Shahid Beheshti University

df[df['University'] == "Shahid Beheshti University"].groupby('Category_1')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)

	count	mean	max	min	std
Category_1
11 Medical and Health Sciences	895	6.423464	764	0	37.688060
03 Chemical Sciences	661	1.996974	60	0	3.674646
09 Engineering	600	1.428333	97	0	4.800688
02 Physical Sciences	566	2.994700	219	0	11.449233
06 Biological Sciences	496	6.508065	1410	0	64.434008
08 Information and Computing Sciences	379	1.897098	191	0	10.525226
01 Mathematical Sciences	343	1.472303	19	0	2.475309
17 Psychology and Cognitive Sciences	169	5.532544	220	0	18.026117
04 Earth Sciences	165	2.375758	25	0	4.008149
05 Environmental Sciences	121	2.768595	35	0	4.900273
10 Technology	76	1.184211	10	0	1.902353
16 Studies in Human Society	52	2.134615	16	0	3.211675
07 Agricultural and Veterinary Sciences	44	2.250000	13	0	3.170540
14 Economics	43	0.906977	7	0	1.394102
00 No Category	34	2.205882	69	0	11.813653
13 Education	29	1.655172	9	0	2.334154
15 Commerce, Management, Tourism and Services	27	1.740741	14	0	2.781707
12 Built Environment and Design	27	1.555556	22	0	4.181768
20 Language, Communication and Culture	21	1.714286	18	0	3.887710
40 Engineering	10	0.400000	3	0	0.966092
22 Philosophy and Religious Studies	10	0.200000	1	0	0.421637
21 History and Archaeology	7	1.571429	3	0	1.272418
18 Law and Legal Studies	5	0.800000	3	0	1.303840
49 Mathematical Sciences	3	0.333333	1	0	0.577350
31 Biological Sciences	2	0.500000	1	0	0.707107
32 Biomedical and Clinical Sciences	2	2.000000	3	1	1.414214
33 Built Environment and Design	1	2.000000	2	2	NaN
34 Chemical Sciences	1	1.000000	1	1	NaN
42 Health Sciences	1	1.000000	1	1	NaN
19 Studies in Creative Arts and Writing	1	0.000000	0	0	NaN
50 Philosophy and Religious Studies	1	1.000000	1	1	NaN
51 Physical Sciences	1	2.000000	2	2	NaN

Ferdowsi University of Mashhad

df[df['University'] == "Ferdowsi University of Mashhad"].groupby('Category_1')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)

	count	mean	max	min	std
Category_1
09 Engineering	913	1.197152	77	0	3.189984
06 Biological Sciences	705	4.012766	220	0	14.603417
11 Medical and Health Sciences	657	5.474886	357	0	25.807089
03 Chemical Sciences	628	1.815287	131	0	5.690667
08 Information and Computing Sciences	409	3.493888	1017	0	50.272547
01 Mathematical Sciences	352	0.920455	10	0	1.588575
02 Physical Sciences	259	2.467181	63	0	4.962835
04 Earth Sciences	230	4.517391	241	0	19.943964
05 Environmental Sciences	157	11.280255	538	0	52.470240
07 Agricultural and Veterinary Sciences	151	1.761589	24	0	3.097760
17 Psychology and Cognitive Sciences	99	4.676768	139	0	14.819890
10 Technology	54	1.333333	9	0	2.136829
15 Commerce, Management, Tourism and Services	52	2.134615	82	0	11.341454
13 Education	51	1.333333	10	0	1.544884
14 Economics	44	0.659091	5	0	0.963115
16 Studies in Human Society	35	2.514286	37	0	6.227885
20 Language, Communication and Culture	27	2.111111	16	0	3.377907
12 Built Environment and Design	20	0.900000	3	0	0.967906
00 No Category	14	4.285714	25	0	8.165863
22 Philosophy and Religious Studies	12	6.750000	46	0	12.892034
21 History and Archaeology	9	1.888889	5	0	1.763834
32 Biomedical and Clinical Sciences	5	9.400000	23	0	9.555103
40 Engineering	5	0.000000	0	0	0.000000
19 Studies in Creative Arts and Writing	4	3.500000	8	0	3.696846
31 Biological Sciences	4	4.250000	11	0	4.716991
34 Chemical Sciences	2	0.500000	1	0	0.707107
35 Commerce, Management, Tourism and Services	2	3.000000	3	3	0.000000
50 Philosophy and Religious Studies	2	1.000000	1	1	0.000000
37 Earth Sciences	1	3.000000	3	3	NaN
38 Economics	1	1.000000	1	1	NaN
39 Education	1	3.000000	3	3	NaN
30 Agricultural, Veterinary and Food Sciences	1	3.000000	3	3	NaN
44 Human Society	1	1.000000	1	1	NaN
46 Information and Computing Sciences	1	0.000000	0	0	NaN
49 Mathematical Sciences	1	0.000000	0	0	NaN
52 Psychology	1	1.000000	1	1	NaN

University of Guilan

df[df['University'] == "University of Guilan"].groupby('Category_1')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)

	count	mean	max	min	std
Category_1
09 Engineering	370	1.621622	100	0	5.938033
06 Biological Sciences	361	4.277008	295	0	19.403772
11 Medical and Health Sciences	288	9.291667	790	0	50.066976
03 Chemical Sciences	282	1.652482	14	0	1.842503
02 Physical Sciences	231	1.333333	31	0	3.212295
01 Mathematical Sciences	109	1.055046	13	0	1.819823
08 Information and Computing Sciences	102	1.166667	22	0	2.552744
05 Environmental Sciences	74	3.324324	182	0	21.081127
07 Agricultural and Veterinary Sciences	62	1.483871	10	0	2.078318
04 Earth Sciences	40	1.150000	12	0	2.019774
17 Psychology and Cognitive Sciences	25	3.200000	24	1	5.730038
16 Studies in Human Society	21	1.666667	9	0	1.906130
10 Technology	18	1.055556	3	0	0.937595
13 Education	15	3.600000	34	0	8.517209
15 Commerce, Management, Tourism and Services	11	0.909091	3	0	0.943880
00 No Category	10	2.400000	17	0	5.253570
12 Built Environment and Design	7	1.000000	1	1	0.000000
14 Economics	7	1.000000	3	0	1.000000
20 Language, Communication and Culture	4	3.500000	4	2	1.000000
22 Philosophy and Religious Studies	3	1.333333	3	0	1.527525
31 Biological Sciences	3	1.333333	2	1	0.577350
18 Law and Legal Studies	2	0.500000	1	0	0.707107
34 Chemical Sciences	2	2.000000	3	1	1.414214
21 History and Archaeology	1	0.000000	0	0	NaN
32 Biomedical and Clinical Sciences	1	1.000000	1	1	NaN

Imam Hossein University

df[df['University'] == "Imam Hossein University"].groupby('Category_1')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)

	count	mean	max	min	std
Category_1
03 Chemical Sciences	44	2.090909	10	0	2.009491
11 Medical and Health Sciences	26	2.846154	25	0	5.334359
09 Engineering	14	1.000000	6	0	1.797434
02 Physical Sciences	11	1.272727	6	0	1.618080
01 Mathematical Sciences	7	0.714286	2	0	0.755929
08 Information and Computing Sciences	7	1.285714	3	0	1.253566
17 Psychology and Cognitive Sciences	6	3.166667	11	0	4.490731
06 Biological Sciences	4	2.250000	4	1	1.500000
00 No Category	1	3.000000	3	3	NaN
04 Earth Sciences	1	0.000000	0	0	NaN
07 Agricultural and Veterinary Sciences	1	0.000000	0	0	NaN
12 Built Environment and Design	1	1.000000	1	1	NaN
14 Economics	1	0.000000	0	0	NaN
34 Chemical Sciences	1	3.000000	3	3	NaN

University of Isfahan

df[df['University'] == "University of Isfahan"].groupby('Category_1')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)

	count	mean	max	min	std
Category_1
11 Medical and Health Sciences	793	7.934426	623	0	35.987242
03 Chemical Sciences	487	1.874743	55	0	3.833832
09 Engineering	395	1.579747	74	0	4.661713
06 Biological Sciences	349	2.289398	66	0	4.608766
08 Information and Computing Sciences	241	1.560166	47	0	3.763519
01 Mathematical Sciences	173	1.023121	19	0	2.048691
02 Physical Sciences	166	1.072289	12	0	1.305310
17 Psychology and Cognitive Sciences	109	4.908257	117	0	15.432974
04 Earth Sciences	81	2.469136	23	0	4.505792
15 Commerce, Management, Tourism and Services	60	1.133333	8	0	1.770442
13 Education	51	2.313725	17	0	3.770890
10 Technology	34	1.000000	6	0	1.348400
05 Environmental Sciences	32	5.812500	114	0	20.233217
16 Studies in Human Society	29	4.241379	64	0	11.897344
20 Language, Communication and Culture	23	1.826087	9	0	2.405527
21 History and Archaeology	18	1.555556	4	0	1.041618
12 Built Environment and Design	14	0.928571	5	0	1.491735
14 Economics	14	1.357143	5	0	1.736803
00 No Category	9	1.333333	10	0	3.278719
07 Agricultural and Veterinary Sciences	8	0.625000	2	0	0.744024
22 Philosophy and Religious Studies	6	1.166667	3	0	0.983192
18 Law and Legal Studies	3	4.000000	9	0	4.582576
31 Biological Sciences	2	1.500000	3	0	2.121320
44 Human Society	2	2.000000	3	1	1.414214
30 Agricultural, Veterinary and Food Sciences	1	1.000000	1	1	NaN
32 Biomedical and Clinical Sciences	1	0.000000	0	0	NaN
46 Information and Computing Sciences	1	0.000000	0	0	NaN
51 Physical Sciences	1	2.000000	2	2	NaN

Isfahan University of Technology

df[df['University'] == "Isfahan University of Technology"].groupby('Category_1')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)

	count	mean	max	min	std
Category_1
09 Engineering	1287	1.590521	135	0	5.982557
03 Chemical Sciences	793	1.559899	39	0	2.388262
02 Physical Sciences	567	4.128748	238	0	12.154744
01 Mathematical Sciences	449	2.821826	190	0	11.233296
06 Biological Sciences	415	2.392771	86	0	6.073817
08 Information and Computing Sciences	412	0.720874	10	0	1.435474
05 Environmental Sciences	215	2.827907	66	0	8.515141
11 Medical and Health Sciences	148	4.337838	98	0	12.035409
04 Earth Sciences	144	6.236111	372	0	36.190635
07 Agricultural and Veterinary Sciences	117	1.649573	11	0	2.229567
10 Technology	60	1.166667	14	0	2.293444
00 No Category	53	0.264151	6	0	1.162738
12 Built Environment and Design	12	0.833333	1	0	0.389249
14 Economics	11	0.636364	3	0	1.026911
16 Studies in Human Society	9	0.888889	3	0	1.054093
17 Psychology and Cognitive Sciences	7	4.285714	21	0	7.653197
13 Education	5	0.600000	2	0	0.894427
15 Commerce, Management, Tourism and Services	5	1.200000	3	0	1.095445
32 Biomedical and Clinical Sciences	4	0.000000	0	0	0.000000
40 Engineering	4	0.250000	1	0	0.500000
18 Law and Legal Studies	3	16.000000	44	0	24.331050
30 Agricultural, Veterinary and Food Sciences	1	0.000000	0	0	NaN
31 Biological Sciences	1	0.000000	0	0	NaN
33 Built Environment and Design	1	1.000000	1	1	NaN
52 Psychology	1	7.000000	7	7	NaN

K. N. Toosi University of Technology

df[df['University'] == "K. N. Toosi University of Technology"].groupby('Category_1')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)

	count	mean	max	min	std
Category_1
09 Engineering	752	1.285904	63	0	3.891253
08 Information and Computing Sciences	325	1.363077	57	0	3.650282
03 Chemical Sciences	284	1.679577	17	0	2.172622
01 Mathematical Sciences	188	0.851064	11	0	1.447531
11 Medical and Health Sciences	123	3.943089	221	0	19.980031
02 Physical Sciences	120	1.816667	14	0	2.425299
04 Earth Sciences	112	1.732143	12	0	2.471394
10 Technology	58	1.879310	10	0	2.421233
06 Biological Sciences	36	2.055556	11	0	3.134549
05 Environmental Sciences	30	2.733333	17	0	4.101584
14 Economics	15	23.000000	179	0	52.607713
17 Psychology and Cognitive Sciences	10	1.200000	3	0	1.316561
12 Built Environment and Design	9	0.888889	2	0	0.781736
15 Commerce, Management, Tourism and Services	9	2.555556	12	0	3.711843
16 Studies in Human Society	8	0.625000	2	0	0.744024
00 No Category	5	0.200000	1	0	0.447214
07 Agricultural and Veterinary Sciences	5	1.000000	3	0	1.224745
18 Law and Legal Studies	3	1.333333	4	0	2.309401
40 Engineering	3	4.000000	6	0	3.464102
34 Chemical Sciences	2	0.500000	1	0	0.707107
19 Studies in Creative Arts and Writing	1	1.000000	1	1	NaN
20 Language, Communication and Culture	1	0.000000	0	0	NaN
21 History and Archaeology	1	1.000000	1	1	NaN
22 Philosophy and Religious Studies	1	0.000000	0	0	NaN
31 Biological Sciences	1	2.000000	2	2	NaN
32 Biomedical and Clinical Sciences	1	0.000000	0	0	NaN

Sharif University of Technology

df[df['University'] == "Sharif University of Technology"].groupby('Category_1')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)

	count	mean	max	min	std
Category_1
09 Engineering	2001	1.469765	140	0	5.305866
03 Chemical Sciences	1029	2.423712	201	0	8.331604
08 Information and Computing Sciences	1014	1.288955	108	0	4.367647
01 Mathematical Sciences	799	1.630788	102	0	6.030424
02 Physical Sciences	765	4.913725	329	0	21.052363
11 Medical and Health Sciences	431	13.236659	1367	0	85.964972
10 Technology	194	1.788660	140	0	10.129814
06 Biological Sciences	161	5.875776	304	0	24.832126
04 Earth Sciences	97	4.979381	126	0	17.562970
14 Economics	65	1.600000	14	0	2.793967
17 Psychology and Cognitive Sciences	59	1.338983	19	0	3.014964
15 Commerce, Management, Tourism and Services	42	4.642857	141	0	21.725361
05 Environmental Sciences	36	1.694444	14	0	2.925938
00 No Category	30	0.900000	15	0	2.795933
40 Engineering	22	0.772727	6	0	1.823963
16 Studies in Human Society	20	2.100000	10	0	2.900091
13 Education	17	1.823529	7	0	2.038237
12 Built Environment and Design	15	1.466667	6	0	1.684665
22 Philosophy and Religious Studies	14	7.214286	60	0	15.870527
20 Language, Communication and Culture	13	1.769231	6	0	2.087816
49 Mathematical Sciences	8	0.000000	0	0	0.000000
34 Chemical Sciences	6	1.666667	3	0	1.211060
18 Law and Legal Studies	6	1.166667	3	0	1.471960
07 Agricultural and Veterinary Sciences	4	3.500000	10	0	4.509250
46 Information and Computing Sciences	3	0.333333	1	0	0.577350
32 Biomedical and Clinical Sciences	3	0.000000	0	0	0.000000
21 History and Archaeology	3	3.333333	9	0	4.932883
19 Studies in Creative Arts and Writing	3	0.000000	0	0	0.000000
31 Biological Sciences	2	0.000000	0	0	0.000000
35 Commerce, Management, Tourism and Services	1	1.000000	1	1	NaN
36 Creative Arts and Writing	1	0.000000	0	0	NaN
37 Earth Sciences	1	1.000000	1	1	NaN
51 Physical Sciences	1	9.000000	9	9	NaN

Iran University of Science and Technology

df[df['University'] == "Iran University of Science and Technology"].groupby('Category_1')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)

	count	mean	max	min	std
Category_1
09 Engineering	1696	1.650943	507	0	15.099734
08 Information and Computing Sciences	677	0.927622	26	0	2.034268
03 Chemical Sciences	608	1.697368	48	0	3.045594
01 Mathematical Sciences	484	0.756198	21	0	1.562521
11 Medical and Health Sciences	175	2.440000	59	0	5.929238
02 Physical Sciences	170	1.088235	13	0	1.583339
10 Technology	120	0.783333	6	0	1.189473
04 Earth Sciences	76	2.407895	53	0	7.941751
06 Biological Sciences	65	2.476923	34	0	5.229447
05 Environmental Sciences	53	1.396226	15	0	2.256100
12 Built Environment and Design	43	1.279070	14	0	2.693251
15 Commerce, Management, Tourism and Services	43	0.604651	3	0	0.954676
14 Economics	41	0.853659	12	0	2.080396
17 Psychology and Cognitive Sciences	40	3.200000	59	0	9.855885
13 Education	19	1.263158	3	0	0.805682
16 Studies in Human Society	10	1.600000	3	0	1.173788
00 No Category	9	0.333333	3	0	1.000000
20 Language, Communication and Culture	8	0.625000	3	0	1.060660
07 Agricultural and Veterinary Sciences	4	0.000000	0	0	0.000000
32 Biomedical and Clinical Sciences	3	0.000000	0	0	0.000000
46 Information and Computing Sciences	3	0.666667	1	0	0.577350
22 Philosophy and Religious Studies	2	0.500000	1	0	0.707107
35 Commerce, Management, Tourism and Services	2	2.000000	3	1	1.414214
21 History and Archaeology	1	3.000000	3	3	NaN
34 Chemical Sciences	1	0.000000	0	0	NaN
40 Engineering	1	0.000000	0	0	NaN
47 Language, Communication and Culture	1	0.000000	0	0	NaN
49 Mathematical Sciences	1	0.000000	0	0	NaN

University of Tabriz

df[df['University'] == "University of Tabriz"].groupby('Category_1')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)

	count	mean	max	min	std
Category_1
09 Engineering	856	1.386682	78	0	3.819771
03 Chemical Sciences	676	1.390533	16	0	1.822046
11 Medical and Health Sciences	597	4.294807	349	0	20.396165
06 Biological Sciences	524	1.979008	33	0	3.172772
08 Information and Computing Sciences	286	1.146853	11	0	1.732894
01 Mathematical Sciences	266	0.725564	8	0	1.103966
02 Physical Sciences	265	1.769811	57	0	4.081072
04 Earth Sciences	205	1.946341	57	0	5.777072
07 Agricultural and Veterinary Sciences	115	3.234783	202	0	18.886703
05 Environmental Sciences	111	1.378378	19	0	2.475531
17 Psychology and Cognitive Sciences	51	2.764706	20	0	3.957718
14 Economics	44	0.295455	3	0	0.667503
10 Technology	38	2.763158	31	0	6.292141
12 Built Environment and Design	22	1.681818	8	0	2.056033
15 Commerce, Management, Tourism and Services	16	1.062500	4	0	1.340087
16 Studies in Human Society	11	0.636364	3	0	1.026911
13 Education	10	4.600000	28	1	8.369256
40 Engineering	9	0.444444	3	0	1.013794
00 No Category	8	0.500000	3	0	1.069045
20 Language, Communication and Culture	7	2.142857	6	0	2.410295
22 Philosophy and Religious Studies	4	2.250000	6	0	2.872281
31 Biological Sciences	2	1.500000	2	1	0.707107
37 Earth Sciences	2	0.500000	1	0	0.707107
18 Law and Legal Studies	2	0.500000	1	0	0.707107
49 Mathematical Sciences	1	0.000000	0	0	NaN
51 Physical Sciences	1	0.000000	0	0	NaN
52 Psychology	1	1.000000	1	1	NaN

Tarbiat Modares University

df[df['University'] == "Tarbiat Modares University"].groupby('Category_1')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)

	count	mean	max	min	std
Category_1
11 Medical and Health Sciences	2728	5.725073	1367	0	39.696113
06 Biological Sciences	1634	3.654835	554	0	21.676706
03 Chemical Sciences	1219	1.759639	191	0	5.853618
09 Engineering	1166	1.572899	182	0	6.109256
08 Information and Computing Sciences	398	1.153266	13	0	1.814891
04 Earth Sciences	331	2.540785	224	0	17.122217
01 Mathematical Sciences	246	1.150407	14	0	1.924530
05 Environmental Sciences	239	11.364017	961	0	74.816002
02 Physical Sciences	194	1.376289	13	0	2.009731
10 Technology	136	1.794118	50	0	4.960761
07 Agricultural and Veterinary Sciences	135	1.600000	11	0	2.130798
17 Psychology and Cognitive Sciences	86	3.430233	65	0	9.368961
14 Economics	62	3.048387	58	0	8.136998
16 Studies in Human Society	62	4.532258	136	0	17.614932
15 Commerce, Management, Tourism and Services	59	1.779661	60	0	7.900388
13 Education	56	2.000000	21	0	3.668044
12 Built Environment and Design	32	1.968750	36	0	6.286105
22 Philosophy and Religious Studies	17	4.117647	19	0	5.765006
00 No Category	15	1.066667	9	0	2.685056
20 Language, Communication and Culture	13	3.461538	31	0	8.362769
21 History and Archaeology	12	241.166667	2043	0	610.841719
32 Biomedical and Clinical Sciences	7	1.000000	5	0	1.825742
40 Engineering	6	1.333333	3	0	1.366260
31 Biological Sciences	3	1.000000	2	0	1.000000
46 Information and Computing Sciences	3	2.333333	6	0	3.214550
19 Studies in Creative Arts and Writing	2	2.000000	3	1	1.414214
18 Law and Legal Studies	2	22.000000	43	1	29.698485
34 Chemical Sciences	1	2.000000	2	2	NaN
35 Commerce, Management, Tourism and Services	1	0.000000	0	0	NaN
37 Earth Sciences	1	1.000000	1	1	NaN
41 Environmental Sciences	1	1.000000	1	1	NaN
42 Health Sciences	1	1.000000	1	1	NaN
44 Human Society	1	2.000000	2	2	NaN
52 Psychology	1	1.000000	1	1	NaN

University of Tehran

df[df['University'] == "University of Tehran"].groupby('Category_1')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)

	count	mean	max	min	std
Category_1
09 Engineering	3134	1.620294	661	0	12.300163
11 Medical and Health Sciences	2564	7.201248	4568	0	93.731110
06 Biological Sciences	2469	6.144998	2766	0	66.144855
03 Chemical Sciences	1736	1.908410	60	0	3.682534
08 Information and Computing Sciences	1320	1.409848	177	0	5.738639
04 Earth Sciences	919	6.078346	3149	0	104.804675
01 Mathematical Sciences	847	1.573790	109	0	5.616080
02 Physical Sciences	614	2.464169	92	0	6.164508
05 Environmental Sciences	474	4.324895	218	0	16.078689
07 Agricultural and Veterinary Sciences	375	1.640000	63	0	3.780190
17 Psychology and Cognitive Sciences	314	8.219745	333	0	32.669701
15 Commerce, Management, Tourism and Services	268	1.119403	24	0	2.681731
16 Studies in Human Society	236	2.830508	44	0	5.868495
10 Technology	199	1.417085	67	0	5.005241
14 Economics	191	4.916230	409	0	31.699133
00 No Category	98	0.602041	7	0	1.419596
13 Education	90	3.444444	66	0	8.968247
12 Built Environment and Design	81	1.382716	22	0	2.790913
21 History and Archaeology	69	58.202899	2043	0	302.322120
20 Language, Communication and Culture	56	1.928571	31	0	4.548155
22 Philosophy and Religious Studies	34	1.352941	4	0	1.432995
32 Biomedical and Clinical Sciences	20	0.500000	3	0	1.100239
31 Biological Sciences	16	1.562500	5	0	1.504161
40 Engineering	15	1.266667	10	0	2.631313
18 Law and Legal Studies	10	12.300000	56	0	20.265186
46 Information and Computing Sciences	9	0.333333	1	0	0.500000
37 Earth Sciences	7	0.571429	3	0	1.133893
19 Studies in Creative Arts and Writing	4	5.000000	10	1	3.915780
30 Agricultural, Veterinary and Food Sciences	4	3.000000	9	0	4.082483
44 Human Society	4	1.750000	3	1	0.957427
33 Built Environment and Design	3	1.333333	2	1	0.577350
35 Commerce, Management, Tourism and Services	3	1.000000	1	1	0.000000
50 Philosophy and Religious Studies	3	23.000000	66	0	37.269290
51 Physical Sciences	2	0.000000	0	0	0.000000
49 Mathematical Sciences	1	0.000000	0	0	NaN
39 Education	1	1.000000	1	1	NaN
42 Health Sciences	1	1.000000	1	1	NaN
41 Environmental Sciences	1	1.000000	1	1	NaN
38 Economics	1	0.000000	0	0	NaN
52 Psychology	1	1.000000	1	1	NaN